Certainty in perception, reaction and action, and higher order functional connectivity loss in OCD
Poster No:
1820
Submission Type:
Abstract Submission
Authors:
Paolo Lorenzo Belleza1, Hady Saad2, John Kopchick3, Phillip Easter2, David Rosenberg2, Jeffrey Stanley3, Vaibhav Diwadkar2
Institutions:
1Wayne State University, Canton, MI, 2Wayne State University, Detroit, MI, 3Wayne State University, Department of Psychiatry, Detroit, MI
First Author:
Co-Author(s):
Introduction:
Predictive processing is partially linked to the certainty (or uncertainty) of the perception-reaction-action cycle (Friston, 2019). In typical control participants, fixed stimulus-response relationships with low uncertainty evoke smooth resonance in network states (Asemi et al., 2015). However, this resonance is distorted when uncertainty is increased (and the predictability of the perception-action-cycle frays). In conditions like Obsessive Compulsive Disorder (OCD), the clinical core of the illness may impact predictive processing and the relationship between perception-reaction-action cycles and brain network interactions (Soriano-Mas, 2021). Such network interactions have usually been assessed on a bivariate basis using methods like stationary functional connectivity based on zero lag correlations (Silverstein et al., 2016). However, the functional properties of brain networks can be captured at several spatial and temporal scales including high-order functional connectivity (HOFC)(Zhang et al., 2016). In any connectome of n nodes, HOFC quantifies the integrative similarity between any pairs of nodes a and b, by estimating the similarity between the connectivity vector of a and all n nodes, and the connectivity vector of b and all n nodes. Here, we provide the first application of studying how certainty and uncertainty in the perception-reaction-action cycle impacts higher order brain network connectivity in OCD youth and typical controls.
Methods:
fMRI data (Siemens Verio 3T) were acquired in 63 participants (37 OCD and 26 HC) while they performed a simple perception-reaction-action task. The task required responses (finger tap) to a colored square (green or red). The predictability (certainty or uncertainty) associated with responding was manipulated using two variables: 1) Response mode was either fixed ("respond to every square" i.e. "Go") or contextualized ("respond only to a green square", 75% targets, i.e., "No-Go) and 2) the inter-stimulus interval was either fixed ("periodic", 1 s SOA) or pseudo-randomly varied (SOAs were randomly sample around a distribution with a mean of 1 s and sd of .5 s). The combination of factors and conditions result in ordinal changes in certainty: High certainty ("Go: Periodic") to High Uncertainty ("No-Go : Pseudorandom). fMRI data were processed in SPM 12 (standard methods). In preparation for HOFC analyses, in each participant, time series were extracted from 246 functionally defined cerebral parcels (Fan et al., 2016) for the computation of pairwise functional connectivity in each of the four conditions (30,012 pairs). Next, HOFC was computed between each of the 246 FC vectors. The resultant HOFC matrix encodes integrative similarities between all pairs of parcels. Inter-group differences in HOFC in each condition were identified after comparing the observed HOFC t-value for each pair against a null distribution generated for each pair (3500 simulations following random reordering of time series before the full computation of the HOFC pipeline). Significant t-values (± 2.5 s.d. of the null) were identified.
Results:
Results are depicted (and discussed in Figures 1 and 2). Briefly, certainty and uncertainty were expressed differently in driving loss of HOFC in OCD.
Conclusions:
The brain's ability to implement predictive processing depends on its ability to flexibly recruit cortical hierarchies (Muzik and Diwadkar, 2023), and this ability is likely to be lost in clinical conditions like OCD. We demonstrate that connectivity patterns emergent at higher orders may provide insights on the nexus between a) task evoked network function centered around the construct of certainty b) and functional expressions of neuropsychiatric pathology in conditions like OCD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Perception, Attention and Motor Behavior:
Perception: Visual
Keywords:
Data analysis
Design and Analysis
FUNCTIONAL MRI
Informatics
Motor
Obessive Compulsive Disorder
1|2Indicates the priority used for review
Provide references using author date format
Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Chu, C., Xie, S., Laird, A.R., Fox, P.T., Eickhoff, S.B., Yu, C., Jiang, T., 2016. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cereb Cortex 26 (8), 3508-3526.
Friston, K.J., 2019. Waves of prediction. PLoS Biol 17 (10), e3000426.
Muzik, O., Diwadkar, V.A., 2023. Depth and hierarchies in the predictive brain: From reaction to action. Wiley Interdiscip Rev Cogn Sci, e1664.
Silverstein, B., Bressler, S., Diwadkar, V.A., 2016. Inferring the dysconnection syndrome in schizophrenia: Interpretational considerations on methods for the network analyses of fMRI data. Front Psychiatry 7, 132.
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