Drift Rates and dlPFC Activation During A Cognitive Control Task in Early Psychosis
Poster No:
386
Submission Type:
Abstract Submission
Authors:
Jessica Arend1, Zoe Liu1, Olivia Calvin1, Lei Xuan1, Anita Kwashie2, Connor Petricek1, Kaylee Enevold1, Sadrisha Pandit1, Andrew Brown1, Bryon Mueller1, Angus MacDonald III1
Institutions:
1University of Minnesota, Minneapolis, MN, 2Icahn School of Medicine at Mount Sinai, New York, NY
First Author:
Co-Author(s):
Introduction:
Cognitive control is impaired in people with psychosis (PwP) (MacDonald et al., 2003). These deficits are underpinned by failures in information representation and maintenance, which are associated with reduced activity in the dorsolateral prefrontal cortex (dlPFC; MacDonald et al., 2000). Computationally, drift diffusion modeling (DDM) may clarify mechanisms underlying cognitive control. DDM integrates accuracy and reaction times to model within-subject variability in task performance. It estimates various parameters including drift rate, or the rate at which evidence is accumulated for decision-making (Ratcliff & McKoon, 2008). Previous literature shows that PwP have slower drift rates (Shen et al., 2024; Smucny et al., 2023) that correspond to reduced dlPFC activation during cognitive control tasks (Smucny et al., 2023).
Methods:
The translational orientation pattern expectancy (TOPX) task is a novel variant of the expectancy AX task paradigm (Lopez-Garcia et al., 2016). Participants must press buttons to correctly identify a predominant target sequence of gabor stimuli (an "A" cue followed by an "X" probe, or AX) among rarer non-targets (AY, BX, BY; see figure). BX trials require greater proactive control and AY trials require greater reactive control.
79 participants (40 recently diagnosed PwP and 39 controls) completed two TOPX fMRI scans at 3T. Each TOPX scan included 100 trials and lasted 7 minutes. fMRI data were preprocessed using the HCP pipeline (Glasser et al., 2013). This included parcellation with the Schaefer 400 atlas (Schaefer et al., 2018) followed by model estimation using the general linear model at the scan level combined at the session level. Only trial stimuli associated with correct responses were included in the model. Per parcel z-score for the B-A contrast (B>A) was averaged across Control A parcels located in the dlPFC.
79 participants (40 recently diagnosed PwP and 39 controls) completed two TOPX fMRI scans at 3T. Each TOPX scan included 100 trials and lasted 7 minutes. fMRI data were preprocessed using the HCP pipeline (Glasser et al., 2013). This included parcellation with the Schaefer 400 atlas (Schaefer et al., 2018) followed by model estimation using the general linear model at the scan level combined at the session level. Only trial stimuli associated with correct responses were included in the model. Per parcel z-score for the B-A contrast (B>A) was averaged across Control A parcels located in the dlPFC.
·Translational Orientation Pattern eXpectancy (TOPX) Task
Results:
PwP trended towards lower accuracy on the proactive task trials (BX) compared to controls (t(67.59)=-1.91, p=.061). We fit the same hierarchical DDM (hDMM) as Shen et al. (2023). Linear mixed-effects regression models of hDDM parameters showed that PwP had slower drift rates (F(1,77)=8.07, p=.006) than controls.
PwP did not show significantly reduced dlPFC activation during proactive conditions (B>A) compared to controls (t(75.01)=1.52, p=.134). However, drift rate was predicted by a three-way interaction effect of dlPFC activation, trial condition, and group membership (F(3,225)=2.92, p=.035). We observed a trending main effect of dlPFC activation on drift rate (F(1,75)=3.50, p=0.06) and a significant main effect of trial condition on drift rate (F(3,225)=30.29, p<.001; AX>BY>BX>AY).
PwP did not show significantly reduced dlPFC activation during proactive conditions (B>A) compared to controls (t(75.01)=1.52, p=.134). However, drift rate was predicted by a three-way interaction effect of dlPFC activation, trial condition, and group membership (F(3,225)=2.92, p=.035). We observed a trending main effect of dlPFC activation on drift rate (F(1,75)=3.50, p=0.06) and a significant main effect of trial condition on drift rate (F(3,225)=30.29, p<.001; AX>BY>BX>AY).
Conclusions:
These findings provide a partial replication of previous research. PwP demonstrated lower drift rates than controls on a cognitive control task; that is, they accumulated evidence more slowly. People with lower drift rates trended towards reduced dlPFC activation, although this relationship depended on group membership and task conditions. Applications of this research are limited by our small sample size; future research will seek to reproduce these associations in a larger sample. However, these findings suggest that computational approaches can clarify our understanding of cognitive control deficits and their neural underpinnings.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Other Methods
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
ADULTS
Cognition
Computational Neuroscience
Cortex
DISORDERS
FUNCTIONAL MRI
Modeling
Psychiatric
Psychiatric Disorders
Schizophrenia
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.
Lopez-Garcia, P., Lesh, T. A., Salo, T., Barch, D. M., MacDonald, A. W., Gold, J. M., Ragland, J. D., Strauss, M., Silverstein, S. M., & Carter, C. S. (2016). The neural circuitry supporting goal maintenance during cognitive control: A comparison of expectancy AX-CPT and dot probe expectancy paradigms. Cognitive, Affective & Behavioral Neuroscience, 16(1), 164–175.
MacDonald, A. W., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the Role of the Dorsolateral Prefrontal and Anterior Cingulate Cortex in Cognitive Control. Science, 288(5472), 1835–1838.
MacDonald, A. W., Pogue-Geile, M. F., Johnson, M. K., & Carter, C. S. (2003). A specific deficit in context processing in the unaffected siblings of patients with schizophrenia. Archives of General Psychiatry, 60(1), 57–65.
Ratcliff, R., & McKoon, G. (2008). The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks. Neural Computation, 20(4), 873–922.
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28(9), 3095–3114.
Shen, C., Calvin, O. L., Rawls, E., Redish, A. D., & Sponheim, S. R. (2024). Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling and Attractor Dynamics. Schizophrenia Bulletin, sbae014.
Smucny, J., Hanks, T. D., & Carter, C. S. (2023). Altered Associations Between Task Performance and Dorsolateral Prefrontal Cortex Activation During Cognitive Control in Schizophrenia. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 8(10), 1050–1057.