Multiple Brain Activation Patterns for the Same Perceptual Decision-Making Task
Poster No:
1048
Submission Type:
Abstract Submission
Authors:
Johan Nakuci1, Jiwon Yeon2, Nadia Haddara3, Ji-Hyun Kim4, Sung-Phil Kim4, Dobromir Rahnev3
Institutions:
1US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, 2Stanford University, Palo Alto, CA, 3Georgia Institute of Technology, Atlanta, GA, 4Ulsan National Institute of Science and Technology, Ulsan, Ulsan
First Author:
Co-Author(s):
Introduction:
Brain activity elicited by the same stimulus or task is highly variable (REF). However, most studies assume a single activation pattern, often identified via standard general linear modeling (GLM). This approach treats trial-to-trial variability as noise. For externally focused attention tasks, GLM has identified "task-positive" regions that increase in activity and "task-negative" regions that decrease in activity.
Here we leveraged trial-to-trial fMRI fluctuations to test whether distinct sets of brain regions are activated across trials during the same task. Across three perceptual decision-making experiments, we estimated brain activations for each trial and used modularity-maximization clustering to group trials based on similarity. In all experiments, we found multiple distinct, stable trial subtypes, suggesting the same task can involve widely varying activation patterns.
Here we leveraged trial-to-trial fMRI fluctuations to test whether distinct sets of brain regions are activated across trials during the same task. Across three perceptual decision-making experiments, we estimated brain activations for each trial and used modularity-maximization clustering to group trials based on similarity. In all experiments, we found multiple distinct, stable trial subtypes, suggesting the same task can involve widely varying activation patterns.
Methods:
Subjects and Tasks
In Experiment 1 as detailed in Nakuci et. al., 50 participants (Ntrial = 768; Ntotal = 35,682 trials) performed a perceptual discrimination task, identifying the dominant color among red or blue dots with varying ratios3. Experiment 2 involved 39 participants (Ntrial = 804; Ntotal = 31,024 trials), and Experiment 3 included 40 participants (Ntrial = 250; Ntotal = 9,959 trials), both performing a motion discrimination task. Details of Experiment 3 are in Yeon et al.4 and Haddara & Rahnev5.
MRI Acquisition
Data were collected using a 3T Siemens Prisma system. T1-weighted anatomical images were acquired using an MP-RAGE sequence with the following parameters:
• Experiment 1: FoV = 256 mm; TR = 2300 ms; TE = 2.28 ms; 192 slices; flip angle = 8°; voxel size = 1.0 x 1.0 x 1.0 mm3
• Experiment 2 and 3: FoV = 256 mm; TR = 2530 ms; TE = 1.69 ms; 176 slices; flip angle = 7˚; voxel size = 1.0 × 1.0 × 1.0 mm3.
•
Functional data acquisition used T2*-weighted sequences with varying parameters:
• Experiment 1: TR = 2000 ms; TE = 35 ms; multiband factor = 3; voxel size = 2.0 mm³.
• Experiment 2: TR = 1200 ms; TE = 30 ms; voxel size = 2.5 mm³.
• Experiment 3: Similar to Experiment 2 with additional multiband factor = 3.
MRI Preprocessing
Data preprocessing in SPM12 included de-spiking, slice-timing correction, realignment, normalization, and spatial smoothing (10 mm FWHM for Experiments 1–2, 6 mm for Experiment 3). Structural images underwent skull removal, normalization to MNI space, and segmentation into tissue types.
Single-Trial Beta Estimation
Using GLMsingle, we estimated trial-specific beta responses, incorporating motion parameters, global signal, and nuisance regressors6.
Clustering
Trial beta maps were pooled across participants to ensure consistency. A similarity matrix based on Pearson correlation was clustered using modularity-maximization (Generalized Louvain algorithm). Consensus clustering identified representative partitions.
In Experiment 1 as detailed in Nakuci et. al., 50 participants (Ntrial = 768; Ntotal = 35,682 trials) performed a perceptual discrimination task, identifying the dominant color among red or blue dots with varying ratios3. Experiment 2 involved 39 participants (Ntrial = 804; Ntotal = 31,024 trials), and Experiment 3 included 40 participants (Ntrial = 250; Ntotal = 9,959 trials), both performing a motion discrimination task. Details of Experiment 3 are in Yeon et al.4 and Haddara & Rahnev5.
MRI Acquisition
Data were collected using a 3T Siemens Prisma system. T1-weighted anatomical images were acquired using an MP-RAGE sequence with the following parameters:
• Experiment 1: FoV = 256 mm; TR = 2300 ms; TE = 2.28 ms; 192 slices; flip angle = 8°; voxel size = 1.0 x 1.0 x 1.0 mm3
• Experiment 2 and 3: FoV = 256 mm; TR = 2530 ms; TE = 1.69 ms; 176 slices; flip angle = 7˚; voxel size = 1.0 × 1.0 × 1.0 mm3.
•
Functional data acquisition used T2*-weighted sequences with varying parameters:
• Experiment 1: TR = 2000 ms; TE = 35 ms; multiband factor = 3; voxel size = 2.0 mm³.
• Experiment 2: TR = 1200 ms; TE = 30 ms; voxel size = 2.5 mm³.
• Experiment 3: Similar to Experiment 2 with additional multiband factor = 3.
MRI Preprocessing
Data preprocessing in SPM12 included de-spiking, slice-timing correction, realignment, normalization, and spatial smoothing (10 mm FWHM for Experiments 1–2, 6 mm for Experiment 3). Structural images underwent skull removal, normalization to MNI space, and segmentation into tissue types.
Single-Trial Beta Estimation
Using GLMsingle, we estimated trial-specific beta responses, incorporating motion parameters, global signal, and nuisance regressors6.
Clustering
Trial beta maps were pooled across participants to ensure consistency. A similarity matrix based on Pearson correlation was clustered using modularity-maximization (Generalized Louvain algorithm). Consensus clustering identified representative partitions.
Results:
In Experiment 1, standard GLM analysis identified increased activity in the visual and motor cortices and decreased brain activity in the orbital frontal cortex and in the posterior cingulate cortex (Fig. 1A). However, single-trial responses deviated substantially from the group map (Fig. 1B). For example, trial 2 showed strong positive activity in both the posterior cingulate and the orbitofrontal cortex. Whereas trial 6 produced an activation pattern similar to the group map.
Clustering based single-trial activation maps, identified multiple distinct but stable subtypes of trials (Fig. 1C-E). Surprisingly, one of the subtypes exhibits strong activation in the default mode network. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes. Analyses in Experiments 2 and 3 confirmed these findings.
Clustering based single-trial activation maps, identified multiple distinct but stable subtypes of trials (Fig. 1C-E). Surprisingly, one of the subtypes exhibits strong activation in the default mode network. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes. Analyses in Experiments 2 and 3 confirmed these findings.
Conclusions:
These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.
Higher Cognitive Functions:
Decision Making 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Other Methods
Keywords:
Cognition
Data analysis
FUNCTIONAL MRI
Perception
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
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.
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.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Arieli, A., Sterkin, A., Grinvald, A., & Aertsen, A. (1996). Dynamics of ongoing activity: Explanation of the large variability in evoked cortical responses. Science, 273(5283), 1868–1871. https://doi.org/10.1126/science.273.5283.1868
Friston, K. J., Holmes, A. P., Price, C. J., Büchel, C., & Worsley, K. J. (1999). Multisubject fMRI Studies and Conjunction Analyses. NeuroImage, 10(4), 385–396.
Goris, R. L. T., Movshon, J. A., & Simoncelli, E. P. (2014). Partitioning neuronal variability. Nature Neuroscience, 17(6), 858–865. https://doi.org/10.1038/nn.3711
Haddara, N., & Rahnev, D. (2024). Threat Expectation Does Not Improve Perceptual Discrimination despite Causing Heightened Priority Processing in the Frontoparietal Network. The Journal of Neuroscience, 44(15), e1219232023. https://doi.org/10.1523/JNEUROSCI.1219-23.2023
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J. P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328, 876–878. https://doi.org/10.1126/science.1184819
Nakuci, J., Yeon, J., Xue, K., Kim, J.-H., Kim, S.-P., & Rahnev, D. (2023). Quantifying the contribution of subject and group factors in brain activation. Cerebral Cortex, 33(12), 11092–11101. https://doi.org/10.1093/cercor/bhad348
Prince, J. S., Charest, I., Kurzawski, J. W., Pyles, J. A., Tarr, M. J., & Kay, K. N. (2022). Improving the accuracy of single-trial fMRI response estimates using GLMsingle. eLife, 11, e77599. https://doi.org/10.7554/eLife.77599
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Yeon, J., Shekhar, M., & Rahnev, D. (2020). Overlapping and unique neural circuits are activated during perceptual decision making and confidence. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-77820-6