Poster No:
759
Submission Type:
Abstract Submission
Authors:
Bonnie Ng1, Sam Doesburg1, Bernard Crespi1, Nancy Yang1, Vasily Vakorin1, Sylvain Moreno1
Institutions:
1Simon Fraser University, Vancouver, British Columbia
First Author:
Bonnie Ng
Simon Fraser University
Vancouver, British Columbia
Co-Author(s):
Sam Doesburg
Simon Fraser University
Vancouver, British Columbia
Nancy Yang
Simon Fraser University
Vancouver, British Columbia
Introduction:
The biological basis of individual variations in religiosity remains poorly understood. Research highlights specific brain networks and regions, including the default mode network (DMN), precuneus, and anterior cingulate cortex (Gaw, 2019; Han et al., 2008; Harris et al., 2009), as linked to religiosity. Notably, these brain regions overlap with those implicated in autistic and positive schizotypal traits (Padmanabhan et al., 2017; Wang et al., 2020). In non-clinical populations, these traits reflect different cognitive styles: systemizing (rule-based, cause-and-effect reasoning) and mentalizing (imagination and Theory of Mind). Higher positive schizotypy and mentalizing are linked to higher levels of religiosity (Harris et al., 2009; Lindeman et al., 2013; Lindeman & Lipsanen, 2016).
Based on these insights, we hypothesize that non-clinical individuals with different levels of autistic and positive schizotypal traits exhibit different levels of religiosity and distinct brain network activity pattern associated with their religiosity. This study investigates these relationships using fMRI and self-report religiosity questionnaires.
Methods:
We recruited participants aged 18 to 42 years. The study was divided into two phases. In phase 1, participants were evaluated using the Autism Quotient (AQ) questionnaire and Schizotypal Personality (SP) questionnaire. AQ/ SP score ratios >± 1.75 defined experimental groups for phase 2: AQ bias (n = 58, mean age = 21.8, # of females = 34) and SP bias (n = 48, mean age = 19.7, # of females = 33). Phase 2 involves structural and resting-state functional MRI using a Philips Ingenia CX 3T scanner. After preprocessing with fMRIprep (v23.1.3), time series for 148 cortical Destrieux atlas ROIs were extracted using FreeSurfer. For each pair of ROIs, we computed a measure of functional connectivity estimated as distance correlation between two fMRI time series, yielding a symmetrical 148*148 matrix for each participant. We then normalized these connectivity matrices across participants by double-centering individual matrices. Degree of religiosity was assessed using self-report questionnaires, which included Brief Multidimensional Measure of Religiousness/ Spirituality (BMMRS), Santa Clara Strength of Religious Faith Questionnaire, and the Baylor Religion Survey (Plante, 2010; Wideman et al., 2013).
Group religiosity differences were assessed using independent samples t-test. The difference in brain-religiosity relationship between AQ and SP groups was also assessed. From each group, 150 random samples, each being 80% of the group's total sample size, were drawn. For each sample, we applied behavioural partial least squares (PLS) (McIntosh & Lobaugh, 2004) to compute a vector of overall correlations between the fMRI connectivity and religiosity. Each sample was treated as an individual observation, and we applied a mean-centered PLS analysis to evaluate the group differences in terms of correlations between the fMRI connectivity and religiosity.
Results:
Our analysis shows that AQ and SP bias groups differ significantly in level of religiosity, with AQ lower and SP higher.
Mean-squared PLS analysis showed distinct brain-religiosity relationships between groups, highlighting differences in terms of local and global brain connections specifically relating to the DMN and medial temporal brain regions.
Conclusions:
This study reveals religiosity differences and distinct brain-religiosity relationship between AQ and SP bias groups. These results suggest that autistic and positive schizotypy-related traits are related not only to individual religiosity levels, but also to neural mechanisms associated with religious thoughts and behaviour. This study advances our understanding of how individual differences in cognitive traits are related to the interplay between brain networks and religiosity in non-clinical populations.
Emotion, Motivation and Social Neuroscience:
Social Cognition
Social Neuroscience Other 2
Higher Cognitive Functions:
Imagery
Higher Cognitive Functions Other 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Keywords:
Autism
Cognition
Computational Neuroscience
FUNCTIONAL MRI
MRI
Multivariate
Perception
Schizophrenia
Other - Default Mode Network
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.
Resting state
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:
Functional MRI
Behavior
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
AFNI
Free Surfer
Other, Please list
-
fMRIprep
Provide references using APA citation style.
Gaw, A. C. (2019). Religious Belief at the Level of the Brain: Neural Correlates and Influence of Culture. Journal of Nervous & Mental Disease, 207(7), 604–610. https://doi.org/10.1097/NMD.0000000000001016
Han, S. (2008). Neural consequences of religious belief on self-referential processing. Social Neuroscience, 3(1), 1–15. https://doi.org/10.1080/17470910701469681
Harris, S. (2009). The Neural Correlates of Religious and Nonreligious Belief. PLoS ONE, 4(10), e7272. https://doi.org/10.1371/journal.pone.0007272
Lindeman, M. (2016). Diverse Cognitive Profiles of Religious Believers and Nonbelievers. The International Journal for the Psychology of Religion, 26(3), 185–192. https://doi.org/10.1080/10508619.2015.1091695
Lindeman, M. (2013). Is it just a brick wall or a sign from the universe? An fMRI study of supernatural believers and skeptics. Social Cognitive and Affective Neuroscience, 8(8), 943–949. https://doi.org/10.1093/scan/nss096
McIntosh, A. R. (2004). Partial least squares analysis of neuroimaging data: Applications and advances. NeuroImage, 23, S250–S263. https://doi.org/10.1016/j.neuroimage.2004.07.020
Padmanabhan, A. (2017). The Default Mode Network in Autism. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(6), 476–486. https://doi.org/10.1016/j.bpsc.2017.04.004
Plante, T. G. (2010). The Santa Clara Strength of Religious Faith Questionnaire: Assessing Faith Engagement in a Brief and Nondenominational Manner. Religions, 1(1), 3–8. https://doi.org/10.3390/rel1010003
Wang, Y. (2020). Altered default mode network functional connectivity in individuals with co-occurrence of schizotypy and obsessive-compulsive traits. Psychiatry Research: Neuroimaging, 305, 111170. https://doi.org/10.1016/j.pscychresns.2020.111170
Wideman, T. H.. (2013). Brief Multidimensional Measure of Religiousness/Spirituality (BMMRS). In M. D. Gellman & J. R. Turner (Eds.), Encyclopedia of Behavioral Medicine (pp. 267–269). Springer New York. https://doi.org/10.1007/978-1-4419-1005-9_1577
No