ArticlePDF Available

The Relationship Between Individual Differences in Gray Matter Volume and Religiosity and Mystical Experiences: A Pre‐registered Voxel‐based Morphometry Study

Authors:

Abstract

The neural substrates of religious belief and experience are an intriguing though contentious topic. Here we had the unique opportunity to establish the relation between validated measures of religiosity and gray matter volume in a large sample of participants (N = 211). In this registered report we conducted a confirmatory Voxel-Based Morphometry (VBM) analysis to test three central hypotheses regarding the relationship between religiosity and mystical experiences and gray matter volume. The preregisterered hypotheses, analysis plan, preprocessing and analysis code, and statistical brain maps are all available from online repositories. By using a region-of-interest (ROI) analysis, we found no evidence that religiosity is associated with a reduced volume of the orbito-frontal cortex and changes in the structure of the bilateral inferior parietal lobes. Neither did we find support for the notion that mystical experiences are associated with a reduced volume of the hippocampus, the right middle temporal gyrus or with the inferior parietal lobes. A whole-brain analysis furthermore indicated that no structural brain differences were found in association with religiosity and mystical experiences. We believe that the search for the neural correlates of religious beliefs and experiences should therefore shift focus from studying structural brain differences to a functional and multivariate approach.
850
|
Eur J Neurosci. 2020;51:850–865.
wileyonlinelibrary.com/journal/ejn
Received: 3 July 2019
|
Revised: 18 August 2019
|
Accepted: 27 August 2019
DOI: 10.1111/ejn.14563
REGISTERED REPORT STAGE 2
The relationship between individual differences in gray matter
volume and religiosity and mystical experiences: A preregistered
voxel‐based morphometry study
Michielvan Elk1,2
|
LukasSnoek1,2,3
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original
work is properly cited.
© 2019 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Edited by EJN Registered Reports Editors.
Abbreviations: DWI, diffusion‐weighted imaging; GLM, general linear model; IPL, inferior parietal lobe; MRI, magnetic resonance imaging; MTL, middle
temporal lobe; OFC, orbitofrontal cortex; ROI, region‐of‐interest; SPL, superior parietal lobe; ToM, theory of mind; VBM, voxel‐based morphometry;
VMPC, ventromedial prefrontal cortex.
1Department of Psychology,University of
Amsterdam, Amsterdam, The Netherlands
2Amsterdam Brain and Cognition
Center,University of Amsterdam,
Amsterdam, The Netherlands
3Spinoza Center for Neuroimaging,Royal
Netherlands Academy of Arts and Sciences,
Amsterdam, The Netherlands
Correspondence
Michiel van Elk, Department of
Psychology, University of Amsterdam,
Nieuwe Achtergracht 129B, 1018WT
Amsterdam, The Netherlands.
Email: m.vanelk@uva.nl
Funding information
John Templeton Foundation, Grant/Award
Number: # 60663
Abstract
The neural substrates of religious belief and experience are an intriguing though con-
tentious topic. Here, we had the unique opportunity to establish the relation between
validated measures of religiosity and gray matter volume in a large sample of partici-
pants (N=211). In this registered report, we conducted a confirmatory voxel‐based
morphometry analysis to test three central hypotheses regarding the relationship be-
tween religiosity and mystical experiences and gray matter volume. The preregister-
ered hypotheses, analysis plan, preprocessing and analysis code and statistical brain
maps are all available from online repositories. By using a region‐of‐interest analy-
sis, we found no evidence that religiosity is associated with a reduced volume of the
orbito‐frontal cortex and changes in the structure of the bilateral inferior parietal
lobes. Neither did we find support for the notion that mystical experiences are as-
sociated with a reduced volume of the hippocampus, the right middle temporal gyrus
or with the inferior parietal lobes. A whole‐brain analysis furthermore indicated that
no structural brain differences were found in association with religiosity and mys-
tical experiences. We believe that the search for the neural correlates of religious
beliefs and experiences should therefore shift focus from studying structural brain
differences to a functional and multivariate approach.
KEYWORDS
gray matter volume, mystical experience, religiosity, structural brain differences, voxel‐based
morphometry
The peer review history for this article is
available at https ://publo ns.com/publo
n/10.1111/EJN.14563
|
851
van ELK and SnOEK
1
|
INTRODUCTION
In the early 2000s, several newspapers headlined a study that
had found the God‐spot—a brain region that could be con-
sidered the basis of the widespread belief in an omniscient
omnipresent and powerful being. This news was based on
pioneering work by Andrew Newberg, who identified the
neural correlates of the unitary peak experience of monks
(Newberg, Alavi, et al., 2001; Newberg & Iversen, 2003).
One of their key findings was that the superior parietal lobe
(SPL)—a brain region that has been associated with spatial
attention and temporal processing—showed a reduced activ-
ity during meditative peak experiences compared to baseline.
This finding made sense in light of the phenomenological re-
ports that often referred to feelings of a loss of sense of space
and time and the awareness of a presence that was bigger
than the self. These initial results inspired many neuroscien-
tists, philosophers and theologians to reflect on the poten-
tial implications. While some argued that these brain regions
could be considered a mechanism to perceive ultimate reality
(Beauregard & O'Leary, 2007; Newberg, d'Aquili, & Rause,
2001), other researchers gave a more reductionist interpreta-
tion according to which religious belief and mystical experi-
ence could be considered a by‐product of the way our brains
evolved (Boyer, 2003). In this manuscript, we define religios-
ity as the belief in an invisible supernatural agent (i.e., God)
that is typically based on tradition (as united in a community
of believers) and is manifested by overt behavior such as vis-
iting a church or religious meeting and praying on a daily
basis. Mystical experiences are characterized by a reduced
awareness of the self, the loss of sense of space and time and
the feeling of a strong connection with the surrounding world
(Piedmont, 1999).
The debate on the neural correlates of religious belief and
mystical experience has been fueled by other studies that pro-
vided more in‐depth insight in the brain mechanisms at play
in religion. For instance, the observation that religious partic-
ipants recruit brain areas involved in social cognition during
prayer (Schjoedt, Stdkilde‐Jorgensen, Geertz, & Roepstorff,
2009) has led to an impressive literature on the role of hy-
permentalizing as a cognitive bias predisposing people to
become religious (for recent critical review, see: Maij, van
Harreveld, et al., 2017). Similarly, the observation that re-
ligious believers show a reduced brain response to errors
(Inzlicht, McGregor, Hirsh, & Nash, 2009; Inzlicht & Tullett,
2010) has led to the idea that reduced error monitoring and
prefrontal cortex functioning could be associated with the ac-
ceptance of religious ideas. In line with this suggestion, it has
been found that patients with damage to the orbitofrontal cor-
tex (OFC) have a higher likelihood of having encountered a
mystical experience (Cristofori etal., 2016). Thus, the initial
steps toward unraveling the neural substrates of religiosity
appear promising.
At the same time, the neuroscientific study of religion has
been haunted by a lack of methodological rigor (Schjoedt,
2009). Many studies suffer from small sample sizes, a lack
of well‐validated tasks, and conceptual confusion about the
constructs that are measured. As a consequence, it remains
unclear to what extent theories about the neural substrates
underlying religiosity are actually supported by the data (van
Elk & Aleman, 2017). For instance, although several studies
have suggested the involvement of structural temporal lobe
abnormalities in religiosity, the findings are inconclusive: on
the one hand, temporal lobe atrophy has been associated with
increased religiosity by using a region‐of‐interest (ROI) anal-
ysis (Chan etal., 2009; Owen, Hayward, Koenig, Steffens, &
Payne, 2011), while another study found that higher religios-
ity was associated with an increased volume of the temporal
lobe, also by using an ROI voxel‐based morphometry (VBM)
analysis (Kapogiannis, Barbey, Su, Krueger, & Grafman,
2009). Similarly, whereas several neuropsychological le-
sion‐based studies have shown that damage to the inferior
parietal lobe (IPL) is associated with increased spiritual-
ity (Johnstone, Bodling, Cohen, Christ, & Wegrzyn, 2012;
Johnstone & Glass, 2008; Johnstone et al., 2014; Urgesi,
Aglioti, Skrap, & Fabbro, 2010), another VBM study found
that an increased IPL volume was associated with higher
spirituality (Van Schuerbeek, Baeken, De Raedt, De Mey,
& Luypaert, 2011). Thus, the debate on the precise neural
mechanisms involved in religiosity is far from settled.
In the present registered report, we had the unique op-
portunity to assess the relation between well‐validated mea-
surements of religiosity and structural brain differences in a
high‐powered (N=224) study. This allowed us to empirically
test some of the most prominent hypotheses that have been
put forward regarding the neurocognitive basis of religiosity.
The MRI and religiosity data for this project were already col-
lected as part of a larger collaborative research project, but
had not been analyzed in conjunction. Our religiosity scale
included questions related to religious beliefs and practices.
These questions have been used before in previous studies
on religious beliefs and the relation with mentalizing and
agency detection (Maij, van Harreveld etal., 2017; van Elk,
Rutjens, & van Harreveld, 2017). We also included questions
about mystical experiences, including key items taken from
the mysticism scale (Hood, 1975) and the Tellegen absorption
scale (Tellegen & Atkinson, 1974). By using structural brain
scans and voxel‐based morphometry (Ashburner & Friston,
2000), we investigated whether increased religiosity is associ-
ated with structural differences in gray matter volume, both in
a confirmatory approach using ROI analyses of brain regions
suggested by the literature as well using a whole‐brain anal-
ysis. Given the large number of participants in our study, we
were able to draw more robust and precise inferences about the
relation between religiosity and gray and white matter volume
than in previous studies (Cremers, Wager, & Yarkoni, 2017).
852
|
van ELK and SnOEK
The specific hypotheses that we tested were based on a
review of the existing literature on the neurocognitive mech-
anisms involved in religion and spirituality (for detailed re-
view, see: van Elk & Aleman, 2017).
First, we tested whether a reduced volume of the bilateral
orbitofrontal cortex is associated with a stronger endorse-
ment of religious beliefs. This hypothesis follows from the
theoretical framework of predictive processing (van Elk &
Aleman, 2017), as well as from the cognitive resource deple-
tion model (Schjoedt etal., 2013). Central to these theories is
the notion that a process of reduced error monitoring is at the
basis of willingness to accept and believe religious doctrines.
Some neuropsychological studies have indeed shown that
fronto‐temporal dementia and atrophy of the OFC is asso-
ciated with changes in religiosity (Hayward, Owen, Koenig,
Steffens, & Payne, 2011; Miller, Mychack, Seeley, Rosen, &
Boone, 2001). One study found in a small subset of patients
with fronto‐temporal dementia that some of these patients
experienced significant changes in their personality, includ-
ing an increased interest in religiosity (Miller etal., 2001).
In a longitudinal study using structural brain data from 302
participants, it was found that life‐changing religious experi-
ences were associated with a reduction in atrophy of the left
OFC (Hayward etal., 2011). In contrast, in the same study
more frequent participation in public religious worship was
associated with a stronger atrophy of the left OFC—thereby
painting a more complicated picture of the relationship be-
tween the frontal lobes and religiosity. In a small study in-
volving data from 40 participants, it was found that increased
fear of God was associated with a reduced volume of the left
OFC (Kapogiannis, Barbey, Su, Krueger, etal., 2009). And a
clinical study involving data from 103 participants at low or
high risk for depression found that increased importance of
religion and spirituality were associated with increased corti-
cal thickness of the mesial frontal lobe (Miller etal., 2014). A
study with data from 116 patients with traumatic brain injury
found that lesions to the dorsolateral prefrontal cortex and
the middle/superior temporal cortex were associated with in-
creased mysticism (Cristofori etal., 2016). Similarly, it was
found in 119 patients with traumatic brain injury that lesions
of the ventromedial prefrontal cortex (VMPFC, which is ana-
tomically synonymous with the OFC; Phillips, MacPherson,
& Della Sala, 2002) were associated with an increase in reli-
gious fundamentalism (Zhong, Cristofori, Bulbulia, Krueger,
& Grafman, 2017). Finally, a study using data from 40 par-
ticipants with and without non‐clinical psychosis also found
that increased intrinsic religiosity was associated with a re-
duced volume of the OFC (Pelletier‐Baldelli etal., 2014).
Functional brain imaging studies corroborate the notion
that changes in prefrontal cortex functioning are associated
with an increased acceptance of religious ideas. It has been
found for instance that believers compared to skeptics show
a reduced neural response to errors—which was localized to
the anterior cingulate cortex (Inzlicht & Tullett, 2010; Inzlicht
etal., 2009). Furthermore, it has been found that paranormal
believers compared to skeptics showed a reduced activation
of the right inferior frontal gyrus when inferring meaning
in random pictures (Lindeman, Svedholm, Riekki, Raij, &
Hari, 2013) and that religious believers compared to skeptics
showed a stronger reduction in the medial and dorsolateral
prefrontal cortex when listening to a prayer by a charismatic
faith healer (Schjoedt, Stodkilde‐Jorgensen, Geertz, Lund, &
Roepstorff, 2011). On the other hand, it has also been found
that personalized prayer to God by charismatic Christians, ac-
tivates the medial prefrontal cortex (MPFC)—which is con-
sidered to be part of the theory‐of‐mind‐network (Schjoedt
etal., 2009). Similarly, reflecting on God's perceived level
of involvement in the world has also been associated with an
increased activation of the MPFC (Kapogiannis, Barbey, Su,
Zamboni, etal., 2009). However, the apparent inconsistency
between these findings is probably related to differences in
the experimental paradigms that were used to study religi-
osity (i.e., prayer and reflection on traits by definition acti-
vate the theory‐of‐mind‐network). We should also bear in
mind that there is not a one‐to‐one correspondence between
changes in structural brain volume and functional brain data.
In fact, network analysis approaches of functional brain data
(e.g., by using functional or effective connectivity) may be
better suited for capturing the cognitive processes underly-
ing religiosity and mystical experience—as they tap more
directly into the efficiency by which neural networks process
information (Bullmore & Sporns, 2009).
Thus—although there are variable and conflicting find-
ings—overall these studies suggest that a reduced volume
of the frontal cortex—most notably the OFC is associated
with an increase in religiosity. This leads to our first hypoth-
esis that reduced volume in the OFC is associated with an
increase in religious beliefs.
Second, traditionally, abnormalities in temporal lobe
anatomy or function, for example, as observed in patients
with temporal lobe epilepsy, have been associated with in-
creased religiosity (for historical overview, see: Devinsky &
Lai, 2008). It has been reported that patients with temporal
lobe epilepsy can have profound religious experiences, which
have been attributed to spontaneous epileptic spikes in tem-
poral brain areas (Joseph, 2001; Saver & Rabin, 1997). For
instance, a patient suffering from temporal epileptic seizures
reported a conversion experience and receiving messianic
messages (Arzy & Schurr, 2016). Furthermore, as discussed
above, structural changes in the temporal lobe, for example,
due to atrophy, have also been associated with an increase in
religiosity (Chan et al., 2009; Owen et al., 2011). Already
in an early study involving structural brain scans from 33
epilepsy patients, a negative relation was found between
increased religiosity and the volume of the right hippocam-
pus (Wuerfel etal., 2004). Furthermore, some patients with
|
853
van ELK and SnOEK
right temporal lobe atrophy—next to experiencing the usual
symptoms associated with temporal lobe atrophy, such as
semantic dementia—showed hyperreligiosity as well (Chan
et al., 2009). In another study, using neuroanatomical data
from 268 adults it was found that having had a life‐chang-
ing religious experience was associated with a stronger at-
rophy of the hippocampus, as shown by using a VBM ROI
analysis (Owen etal., 2011). In a dataset from 80 healthy
volunteers, increases in the character trait of self‐transcen-
dence have been associated with an increased volume of the
middle temporal gyrus, as well as the inferior parietal gyrus
(Van Schuerbeek etal., 2011). Similarly, data from a study
with 42 healthy older adults showed that higher scores on
the personality trait of self‐transcendence were associated
with a reduced volume of the left fronto‐temporal and pa-
rieto‐temporal cortex (Kaasinen, Maguire, Kurki, Bruck, &
Rinne, 2005).
Together these findings suggest that temporal lobe re-
gions may be specifically involved in the experiential aspects
of religiosity, such as mystical experiences and feelings of
self‐transcendence (Grill‐Spector & Malach, 2004). Thus, in
the present study we tested whether items specifically per-
taining to the experiential aspects of religion (i.e., mystical
experiences that are typically characterized by a loss of sense
of space and time) are related to a reduced volume of tem-
poral brain regions, most notably the hippocampus (Owen
etal., 2011) and the right middle temporal gyrus (Chan etal.,
2009).
Thirdly, we tested whether an increased or decreased
volume of gray matter in the bilateral SPL and inferior pa-
rietal lobes (IPL) is associated with a stronger religiosity
and a higher proneness to having had a mystical‐like expe-
rience. This hypothesis partly follows from the initial work
by Newberg by using functional neuroimaging data to es-
tablish the neural correlates of peak meditative experiences
(Newberg, Alavi etal., 2001; Newberg & Iversen, 2003). He
found that peak experiences of absolute unity are associated
with a reduced blood flow to the superior parietal lobes and
an increased activation of prefrontal areas, which he inter-
preted as being associated with a stronger focused attention.
Other studies have used neuropsychological assessment
techniques as an indirect proxy for superior parietal lobe
functioning to establish a relationship between parietal lobe
atrophy and religiosity (Johnstone & Glass, 2008; Johnstone
etal., 2012, 2014; Urgesi etal., 2010). These studies indi-
cate that a reduced activation or an impaired functioning of
the parietal lobes (including the bilateral SPL and the IPL) is
associated with a higher sensitivity for having spiritual expe-
riences and increased religiosity. The supposed underlying
mechanism is that the parietal lobes support a process of
multi‐sensory integration and are at the basis of bodily self‐
awareness (Blanke, 2012). A disruption of this process could
result in changes in self‐awareness, for example, as observed
during self‐transcendent and out‐of‐body experiences, as
has been frequently observed in the neuropsychological lit-
erature (Blanke, Slater, & Serino, 2015). Only a few neuro-
anatomical studies have been conducted on the relationship
between parietal lobe volume and mystical experience.
Damage to the inferior parietal cortex has been associated
with an increase in the personality trait of self‐transcen-
dence in a group of 48 patients undergoing neurosurgery
(Urgesi etal., 2010). This finding fits well with other studies
on “religion‐by‐proxy” phenomena, such as the feeling of a
presence, that have also been associated with damage to the
inferior parietal lobe (for review, see: Blanke etal., 2015).
On the other hand, several studies also indicate that an
increased volume of the parietal lobes is positively associ-
ated with religion and spirituality. One study, using data from
103 participants, found that increased importance of religios-
ity was associated with an increased volume of the left and
right parietal cortices as well as the left precuneus (Miller
etal., 2014). A different study showed that an increased IPL
volume was associated with higher ratings of spirituality in
a sample of 80 healthy participants (Van Schuerbeek etal.,
2011). Also, doubting God's existence has been associated
with a reduced volume of the right precuneus (Kapogiannis,
Barbey, Su, Krueger etal., 2009)—although the sample size
of this study was small. Thus, the relation between pari-
etal lobe volume and religiosity and mystical experience is
mixed. Therefore, we tested a direction‐unspecific hypothe-
sis, by testing the relation between religious beliefs and mys-
tical experiences in relation to either an increase or a decrease
volume of the inferior parietal lobe.
We note that our theoretical predictions were quite ge-
neric and that the directionality of the expected effects is
open to discussion. Still, we argue that—if there is any value
in the neurocognitive mechanisms outlined above—this
should have become visible in the present analysis, which
could also serve to make more fine‐grained predictions for
future studies. We are well aware that by relating religiosity
to differences in gray matter volume, we somehow regress
to the highly controversial phrenology approach (Jones,
Alfaro‐Almagro, & Jbabdi, 2018). Rather than focusing
on structural brain differences, it might make more sense
to use network measures of brain activity and interaction
between different brain regions, such as functional connec-
tivity (Van Den Heuvel & Pol, 2010). We are very much in
favor of using these techniques in association with religion
and spirituality measures—and we definitely intend to use
them in future studies. But our primary aim here was to
establish the (absence of the) relation between religiosity
and structural brain differences at a level of methodological
and statistical rigor that we hope will set a new standard for
future studies.
854
|
van ELK and SnOEK
Thus, the specific hypotheses that we set out to test were
the following: (a) a stronger acceptance of general religious
beliefs is associated with a reduced volume of the bilateral
orbitofrontal cortex; (b) a higher prevalence of mystical ex-
periences is associated with a reduced volume of the right
middle temporal gyrus and the hippocampus; (c) a higher
prevalence of religious beliefs and mystical experiences is
associated with an altered volume of the bilateral IPL. To test
these predictions, we estimated gray matter volume through-
out the entire brain using VBM and subsequently run both
confirmatory ROI analyses of the relation between ROI‐av-
erage gray matter volume and religiosity and mystical ex-
periences as well as a whole‐brain analysis of the relation
between voxel‐wise gray matter volume and religiosity. The
VBM procedure we used includes standard processing steps
of the T1‐weighted scans, including bias‐correction, skull-
stripping, segregation of gray and white matter, non‐linear
normalization to standard MNI space, and a Jacobian mod-
ulation step to correct for local expansion (or contraction)
due to the non‐linear component of the spatial transforma-
tion (Douaud etal., 2007). The ROIs were defined using the
Harvard–Oxford (sub)cortical probabilistic atlas (Craddock,
James, Holtzheimer, Hu, & Mayberg, 2012; for more details
on the ROI definition, see the Methods section).
The reason for doing ROI analyses on prespecified regions
of interest was to obtain a high‐powered confirmatory test of
the hypotheses derived from the literature. Typically, more
restricted ROI analyses (relative to whole‐brain, voxel‐wise
analyses) increase the statistical power to detect a potential
effect (Cremers etal., 2017). Conducting confirmatory ROI
analyses also allowed us to use Bayesian statistics on ROI‐av-
erage gray matter volume estimates, which provides the op-
portunity to quantify the relative evidence for the presence or
absence of a relationship between religiosity and gray matter
volume, which is not possible in the context of whole‐brain
analyses because no standard software packages for VBM
analyses offer Bayesian statistical tests. The ROI analyses
focused on the following hypotheses which were primarily
derived from the structural brain imaging studies (i.e., rather
than the functional studies) discussed above: (a) a stronger
acceptance of general religious beliefs is associated with a
reduced volume of the orbitofrontal cortex; (b) a higher prev-
alence of mystical experiences is associated with a reduced
volume of the right middle temporal gyrus and the hippocam-
pus; (c) a higher prevalence of mystical experiences is associ-
ated with an altered volume of the inferior parietal lobe.
Next to conducting ROI analyses of prespecified brain re-
gions forwarded by the literature, we also conducted a whole‐
brain, voxel‐wise analysis. We believe this type of analysis is
warranted given the quite unspecific nature of our hypothe-
ses (e.g., next to the orbitofrontal lobe, other prefrontal areas
such as the DLPFC have also been implicated in religiosity).
2
|
METHODS
2.1
|
Overview
An overview of the data collection and analysis procedure is
presented in Figure1. The data collection was already com-
pleted before the start of this project, and the structural MRI
data have been checked visually using established quality
metrics using the MRIQC tool (Esteban etal., 2017a; version
0.10.3) and preprocessed using FMRIPREP (Esteban etal.,
2017b; version 1.0.15). For the present project, we analyzed
the religiosity data to test the specific hypotheses by conduct-
ing an ROI and whole‐brain VBM analysis, focusing on the
relation with religiosity and with mystical experiences.
2.2
|
Participants
Participants were recruited at the University of Amsterdam
and consisted of students. In total 244 participants were
tested, but 33 participants could not be used for the final
analysis because of incomplete (MRI or behavioral) data
or scanner artifacts (dropout rate = 8.2%), yielding a total
sample size of N=211. The age range for participants was
FIGURE 1 Overview of data
acquisition and analysis strategy. Boxes
marked in gray had already been completed
prior to commencing this registered report.
Boxes marked in black represent the
analysis plan that was used for the present
study
|
855
van ELK and SnOEK
20–28 years (mean = 24.18, SD = 1.92). The sample for
this study consisted of 118 female participants and 93 male
participants. All participants provided written informed con-
sent before participating in the study and the experimental
procedure was approved by the local ethics committee at the
Psychology Department at the University of Amsterdam.
2.3
|
Outcome neutral criterion
As an outcome neutral criterion, we used the effect of (self‐
reported) gender on gray matter volume in a separate VBM
analysis. It is well established that there are structural differ-
ences in local and global gray matter structure between the
brains of men of women (Good etal., 2001; Smith, Chebrolu,
Wekstein, Schmitt, & Markesbery, 2007). Note that multi-
variate predictive analyses of the same VBM data have al-
ready shown that gender can be “decoded” from whole‐brain
patterns of gray matter volume (Snoek, Miletic, & Scholte,
2018). While this multivariate analysis is different than the
intended univariate analysis for this outcome neutral crite-
rion, we believe that it demonstrates the validity of the pro-
posed neutral criterion. By testing the main effect of gender
on gray matter volume (by using a whole‐brain, voxel‐wise
analysis on the same VBM data that was used for the re-
ligiosity analysis), we were thus able to show that our data
are suitable for the intended main analysis. We expected to
find widespread gender differences in gray matter volume
throughout the brain (see e.g., Takahashi, Ishii, Kakigi, &
Yokoyama, 2011).
2.4
|
Power analysis
In this project, we first conducted a set of ROI analyses based
on prespecified brain areas that have been implicated in reli-
gious beliefs and mystical experiences. Next, given the rather
broad and unspecific nature of the suggestions in the litera-
ture, we also conducted a whole‐brain analysis (of which the
results were corrected for multiple comparisons).
There are multiple ways in which a power analysis could
be conducted. Here, we based the estimated effect size on
the reported effects in neuroanatomical studies on religios-
ity and mystical experience (Cristofori etal., 2016; Hayward
etal., 2011; Owen etal., 2011; Van Schuerbeek etal., 2011).
Although these papers did not always provide sufficient de-
tail to obtain a standardized effect size, overall the reported
effects were small, that is, β‐values ranged from .12 to .22
(Hayward etal., 2011; Owen et al., 2011), and η2 ranged
from .01 to .07 (Cristofori etal., 2016; Zhong etal., 2017).
Assuming a small effect size for our analysis of r =.20, a
sample size of N=224 and an alpha‐level of p<.05, the
achieved power of our analysis was 1 − β = .92, meaning
that there was 92% chance of correctly rejecting the null hy-
pothesis that there was no relation between religiosity and
brain volume (note, however, that strictly speaking our in-
tended Bayesian analyses do not employ the null‐hypothesis
testing framework assumed by power analyses). This crite-
rion exceeds the critical threshold of at least 80% statistical
power (Cohen, 1992), and we note that our sample size far
exceeds that of most previous studies on this topic. Thereby,
we aimed to provide a more precise estimate of the effect size
regarding the relation between structural brain differences
and religiosity.
2.5
|
Population imaging project
The data for this study were collected as part of the Population
Imaging of Psychology project (PIoP1), which was conducted
at the Spinoza Center for Neuroimaging at the University of
Amsterdam. The aim of the PIoP was to offer researchers the
opportunity to collect brain imaging data from a large sam-
ple of participants (intended N =250), in association with
their individual difference measure of interest. The data were
collected between May 2015 and April 2016. The MRI data
have been preprocessed by LS and have been used already
for a project to identify multivariate structural brain differ-
ences in association with gender (Snoek et al., 2018). The
behavioral data (i.e., religiosity questionnaires) have been ac-
quired by MvE but had not been subjected to any analysis so
far. Both authors have confirmed that the MRI data have not
been associated in any way to the behavioral data and that all
hypotheses and the processing pipeline were developed and
defined prior to data inspection.
Standard MRI measurements that were collected for each
participant included a structural T1‐weighted scan, task‐free
resting state fMRI (6 min), a diffusion‐weighted imaging
(DWI) scan, and different functional localizer scans that
were collected using gradient‐echo EPI sequences, including
the Gender Stroop Task, an emotional matching task (Hariri,
Bookheimer, & Mazziotta, 2000), a working memory task
(Pessoa, Gutierrez, Bandettini, & Ungerleider, 2002) and
the anticipation of negative emotional vs. neutral scenes
(Oosterwijk, 2017). In addition, for each participant back-
ground demographic variables were recorded (gender, age,
socio‐economic status), as well as the NEO‐FFI personality
questionnaire (Costa & MacCrae, 1992) and an intelligence
test (Raven's matrices; Raven, 2000). For the present study,
we included measures related to religiosity and mystical ex-
periences (for description, see below).
2.6
|
Religiosity measures
For this study, we selected 7 items to measure religiosity,
which were completed using a 5‐point Likert scale ranging
from 1= not at all to 5= very much (see Table1). Six out
of these seven questions were directly based on the items
that are used to measure religiosity in the world value survey
856
|
van ELK and SnOEK
(Freese, 2004): three items assessed people's religious be-
liefs (i.e., religiosity, belief in God, belief in afterlife), two
items assessed the importance of people's faith for their lives,
and two items assessed participants’ religious practices (i.e.,
prayer and church visit). Although these questions are not
part of a standardized and validated scale to measure religios-
ity, the face validity of the items is high (e.g., church visit and
prayer refer to easily identifiable behaviors) and the construct
validity can be further guaranteed based on other items that
were included. Next to the religiosity items, we also asked
whether participants considered themselves to be a member
of a church or a religious organization, and if so whether they
could indicate their religious denomination (open response).
In this way, we could establish whether participants who in-
dicate religious membership indeed scored higher on the re-
ligiosity questions.
We also asked three questions about the religious beliefs
(religiosity) and practices (church visit and lifestyle) of the
participants’ parents. Previous studies have shown that one's
parents’ religiosity, specifically the extent to which they
show credibility enhancing displays of their beliefs (e.g.,
wearing religious clothing, going to religious meetings), is
a strong predictor of endorsing religious beliefs (Lanman &
Buhrmester, 2017; Maij, van Harreveld etal., 2017). As such,
determining one's parents’ religiosity provides a good way
to further establish the construct validity of our religiosity
scale. Thus, for the VBM analysis we used the seven religi-
osity questions as presented in Table1 as predictor variables.
In addition, we included 6 items to measure mystical‐like
experiences, which were completed using a 5‐point Likert
scale ranging from “1=not at all” to “5=very much” (see
Table2). These items were items related to mystical expe-
riences from the Tellegen absorption scale (Tellegen &
Atkinson, 1974) and items from the mysticism scale (Hood,
1975). In several studies, it has been found that one's scores
on these items are strongly predictive of self‐induced mys-
tical experiences (van Elk, 2015; Maij & van Elk, 2018;
Maij, van Elk, & Schjoedt, 2017), self‐transcendent feelings
of awe (van Elk, Karinen, Specker, Stamkou, & Baas, 2016)
and hearing the voice of God (Luhrmann, 2011; Luhrmann,
Nusbaum, & Thisted, 2013). Accordingly, for the VBM anal-
ysis of mystical experiences, we used the sumscore of the
six items in Table2 as predictor variables. Next to the ques-
tions that were included in the present analysis, we also asked
questions about the participants’ spirituality, paranormal be-
liefs, conspiracy beliefs, and their level of absorption.
It could well be that average ratings of religiosity and
mystical experiences are non‐normally distributed, as data
were mainly collected from secularized students. However,
we note that this is not an issue for the statistical assumptions
of the analyses on the VBM data, which are based on the gen-
eral linear model (GLM) that assumes normality of the mod-
el's residuals, but not normality of its predictors. Moreover,
given results from earlier studies (see for instance: van Elk,
Rutjens, van der Pligt, & Van Harreveld, 2016) and the fact
that this study's sample consistent of university students, rel-
atively few participants scored high on religiosity and mys-
tical experiences. However, while potential low variance in
the predictor‐of‐interest (i.e., religiosity and mystical expe-
riences) may reduce power (Poldrack, Mumford, & Nichols,
2011), this study's relatively large sample size compensates
for this statistical inefficiency.
2.7
|
VBM processing pipeline
The T1‐weighted scans with a voxel size of 1.0×1.0×1.0mm
were acquired using 3D fast field echo (TR: 8.1 ms, TE:
3.7 ms, flip angle: 8°, FOV: 240 × 188 mm, 220 slices).
The T1‐weighted anatomical scan was bias‐corrected, skull-
stripped and segmented using the FMRIPREP package (ver-
sion 1.0.0; Esteban etal., 2017b)—a Nipype (Gorgolewski
etal., 2011) based tool. Each T1 weighted volume was cor-
rected for bias field using N4BiasFieldCorrection (v2.1.0;
Tustison etal., 2010) and skullstripped using antsBrainEx-
traction.sh v2.1.0 (using the OASIS template). Three tissue
classes were extracted from T1w images using FSL FAST
(v5.0.9; Jenkinson, 2003). From here on, we followed the
TABLE 1 Items included to measure religiosity. All items were
completed by using a 5‐point scale ranging from 1=not at all to
5=very much
To what extent do you consider yourself to be religious?
To what extent do you believe in God or a supernatural being?
To what extent do you believe in life after death?
My faith is important to me
My faith affects my thinking and practice in daily life
I pray daily
I visit a church or religious meeting on a weekly basis
TABLE 2 Items included to measure mystical experiences. All
items were completed by using a 5‐point scale ranging from 1=not at
all to 5=very much
I have had an experience which was both timeless and spaceless
I have had an experience in which something greater than myself
seemed to absorb me
I have had an experience in which I felt myself to be absorbed as
one with all things
I have had an experience, of which I was incapable of being ex-
pressed in words
I have had an experience in which I realized the oneness of myself
with all things
I think I really know what some people mean when they talk about
mystical experiences
|
857
van ELK and SnOEK
“FSL‐VBM” protocol (Douaud et al., 2007) from the FSL
software package (version 5.0.9; Smith et al., 2004). The
gray matter maps were registered to the MNI 152 standard
space using non‐linear registration (Andersson, Jenkinson,
& Smith, 2007). The resulting images were averaged and
flipped along the x‐axis to create a left‐right symmetric,
study‐specific gray matter template. Second, all native gray
matter images were non‐linearly registered to this study‐spe-
cific template and “modulated” to correct for local expan-
sion (or contraction) due to the non‐linear component of the
spatial transformation. The modulated gray matter images
were then smoothed with an isotropic Gaussian kernel with
a sigma of 3mm.
We used a volume‐based approach rather than a surface‐
based approach, to preserve consistency with previous stud-
ies on this topic (Cristofori etal., 2016; Kapogiannis, Barbey,
Su, Krueger etal., 2009; Van Schuerbeek etal., 2011).
2.8
|
ROI analyses
The ROI analyses focused on the following hypotheses: (a)
a stronger acceptance of general religious beliefs is associ-
ated with a reduced volume of the orbitofrontal cortex; (b) a
higher prevalence of mystical experiences is associated with
a reduced volume of the right middle temporal gyrus and the
hippocampus; (c) a higher prevalence of mystical experi-
ences and religiosity is associated with an altered volume of
the bilateral IPL (which we define as the combination of the
angular gyrus and the supramarginal gyrus). ROIs for these
brain areas were identified using the probabilistic Harvard–
Oxford (sub)cortical atlas (see Table3). To create a binary
mask, we thresholded the probabilistic ROIs at 0 (i.e., any
voxel with a non‐zero probability of belonging to that brain
area were included in the binary mask). For each participant,
we averaged the voxel‐wise gray matter volume estimates
within each ROI separately, which served as the dependent
measure for our ROI analyses.
For our ROI analyses, we used a Bayesian ANCOVA
model. We used a Bayesian ANCOVA instead of Bayesian
regression because the statistical program we used, JASP
(Marsman & Wagenmakers, 2017; version 0.9.2), does not
allow for categorical independent variables in their Bayesian
regression implementation, which prevents us from includ-
ing gender as independent (“nuisance”) variable. Next to
gender, we included age and intelligence (operationalized
as the sumscore on the Raven's matrices test) as “nuisance”
variables. The rationale for including these measures as
dummy variables in our analysis is to control for the poten-
tial confound that any religiosity effect might be driven by
other individual differences that are known to be associated
with religiosity: typically females are more religious than
males (Miller & Hoffmann, 1995); older participants tend
to be more religious (Argue, Johnson, & White, 1999); and
people scoring high on intelligence are on average less reli-
gious (Zuckerman, Silberman, & Hall, 2013).
As our main independent variables of interest, we included
our two religiosity measures of interest (i.e., religiosity and
mystical experiences). We reported the Bayes factors for the
model including the main independent variables of interest
compared to the null model containing the nuisance variables
(gender, level of education, intelligence, and age). We ran the
Bayesian ANCOVA analysis for each ROI separately.
2.9
|
Whole‐brain analysis
For the whole‐brain analysis, we used a non‐parametric,
permutation‐based (frequentist) GLM (using 10,000 ran-
dom permutations) with threshold‐free cluster enhancement
(TFCE; Smith & Nichols, 2009) using FSL's “randomize”
tool. Using TFCE‐based statistics instead of regular cluster‐
based statistics allows us to draw inferences on the voxel‐
level, which affords more detailed conclusions of the location
of potential significant correlations with religiosity (Smith &
Nichols, 2009). The TFCE‐values were corrected for mul-
tiple comparisons using the maximum statistic approach in
which voxels were only be considered significant if the ob-
served TFCE test statistic falls within the highest or lowest
2.5th percentile of the distribution of the permuted maximum
statistic values (i.e., voxel‐wise α=.025).
Similar to the ROI analyses, we included gender, age and
intelligence as covariates in our whole‐brain analysis. For
this analysis, we specified two contrasts, one for each main
independent variable of interest, which represent tests of
whether regression coefficients differ from zero. Because the
literature reports both positive and negative correlations be-
tween religiosity measures and gray matter volume, we tested
the contrasts in both directions and adjust the significance
level accordingly (i.e., use an alpha of 0.025 instead of the
conventional 0.05; Chen etal., 2019). Thresholded (i.e., sig-
nificant) results were visualized using the MNI152 (2mm)
TABLE 3 Regions of interest for the ROI analysis to assess the
relation between religious beliefs and mystical experiences and gray
matter volume
Religious beliefs ROIs
Sub‐regions (from Harvard‐
Oxford atlas)
(1) Orbitofrontal cortex
(3) Bilateral inferior parietal
lobes
Bilateral angular gyrus
Bilateral supramarginal gyrus
Mystical experiences ROIs
(1) Hippocampus Bilateral hippocampus
(2) Right middle temporal
gyrus
Right anterior MTL
Right posterior MTL
(3) Bilateral inferior parietal
lobes
Bilateral angular gyrus
Bilateral supramarginal gyrus
858
|
van ELK and SnOEK
template with different colors indicating positive versus neg-
ative effects.
To include religiosity and mystical experiences as regres-
sors in our model, first for each scale we calculated the reli-
ability by using Cronbach's α. Next, the sumscores for each
scale were calculated, which were used as predictors in the
statistical model (from both the ROI analyses and whole‐
brain analysis).
3
|
RESULTS
3.1
|
Data and code availability
Most data and all code for this study are deposited in publicly
available online repositories. All analysis code and code to re-
produce the figures of this manuscript are available from the
project's GitHub repository: https ://github.com/lukas snoek/
Relig iosit yVBM. This repository also contains a csv‐file with
the data to reproduce the ROI analyses (i.e., the ROI‐average
gray matter volume, nuisance variables and religious belief/
mystical experience variables). Unthresholded brain maps
from the whole‐brain analysis of both the outcome neutral test
and main analysis can be viewed and downloaded from this
project's Neurovault repository: https ://ident ifiers.org/neuro
vault.colle ction :5380. Lastly, the project was preregistered on
the open‐science framework (OSF) at https ://osf.io/qzkmh/ .
Below, we describe the results from both the outcome
neutral analyses and the main analyses. The unthresholded
brain maps from the whole‐brain analyses for both the out-
come neutral and main analyses can be found in this study's
neurovault repository and the data for the ROI analyses (i.e.,
the ROI‐average gray matter volume and covariates) can be
found in this study's GitHub repository (see Data and Code
availability).
3.2
|
Deviations from preregistration
Although we planned to use data from N=224 participants
in our analysis, in the end we were only able to include data
from N=211 participants. This was the result of participants
that were missing either MRI data or religiosity data.
3.3
|
Descriptive statistics
For the final analysis, 211 participants (118 females) were
retained. The descriptive variables, including religiosity
and personality characteristics, are presented in Tables 4
and 5. Both the religiosity and the mystical experience
scale showed a good reliability, Cronbach's α=.880 and
α=.877, respectively. As can be seen in the correlation
table, religiosity was negatively correlated with intelli-
gence, and mystical experiences were positively correlated
TABLE 4 Descriptive statistics for the participants included in the VBM analysis (N=211)
Age Raven Religiosity Mystical A C E N O
Mean 24.18 24.47 1.725 2.475 43.93 43.27 44.47 30.79 41.64
Std. deviation 1.924 4.997 0.8093 1.139 5.012 6.900 5.257 7.527 6.072
Minimum 20.00 3.000 1.000 1.000 27.00 22.00 31.00 13.00 28.00
Maximum 28.00 35.00 5.000 5.000 56.00 59.00 56.00 58.00 58.00
Abbreviations: A, agreeableness; C, conscientiousness; E, extraversion; N, neuroticism; O, openness to experience (scores on the NFFI personality questionnaire).
TABLE 5 Correlations between the different variables included in this study
Age Raven Religiosity Mystical A C E N O
Age    
Raven −0.001    
Religiosity 0.013 −0.141*
Mystical −0.107 −0.032 0.232***
A −0.040 0.102 0.095 0.003
C 0.044 −0.071 0.086 0.107 0.198**
E 0.059 0.012 0.042 −0.019 0.207** 0.121
N 0.070 −0.115 0.130 0.044 0.002 −0.209** −0.297***
O −0.022 −0.021 0.061 0.029 0.093 −0.176* −0.059 0.201**
Abbreviations: A, agreeableness; C, conscientiousness; E, extraversion; N, neuroticism; O, openness to experience (scores on the NFFI personality questionnaire).
*p<.05.
**p<.01.
***p<.001.
|
859
van ELK and SnOEK
to religiosity—although overall correlations were small. As
expected, participants who indicated to be a member of a
church scored higher on the religiosity scale (mean=3.38,
SE=0.30) than those who did not (mean=1.73, SE=0.05),
t(209)=8.52, p<.001.
Females in our study were slightly older than males
(mean=24.53, SE= 0.16, and mean = 23.74, SE =0.21,
respectively), t(209) = 2.99, p=.003. There was no effect
of gender on religiosity, but females tended to score lower on
mystical experiences (mean= 2.21, SE =0.10) than males
(mean=2.81, SE=0.12), t(209)=−3.90, p<.001. No dif-
ferences were found between males and females on the NNFI
personality traits, t(209)<1.36, p>.174.
3.4
|
Outcome neutral results
For the outcome neutral test, we investigated the effect of
(self‐reported) gender on gray matter volume in a whole‐
brain non‐parametric voxel‐wise analysis using the rand-
omize function from the FSL software package. In Figure2,
we plot the significantly different voxels (two‐sided t test)
resulting from this analysis.
3.5
|
ROI analyses
Our ROI analyses for religious belief were done on the bilat-
eral OFC and the bilateral IPL, while the ROI analyses for
mystical experience were done on the bilateral hippocampus,
right MTL and bilateral IPL (see Figure3).
The ROI analyses are based on average gray matter
volume within a particular ROI. We used the Bayesian
ANCOVA module in the statistical software package “JASP”
for our ROI analyses (Love etal., 2015; Morey & Rouder,
2015; Rouder, Morey, Speckman, & Province, 2012). In the
Bayesian ANCOVA analysis, we used the ROI‐average gray
matter volume as dependent variable, gender as fixed factor,
and intelligence, age, and religious belief or mystical expe-
rience as covariates. The variables gender, intelligence and
age were added to the “null model,” which we compared to
our “religious belief model,” in which we include the reli-
gious belief covariate or “mystical experience model,” in
which we include the mystical experience covariate.
3.5.1
|
Religious belief
For both the OFC and IPL, there was more evidence for the
null model than for the “religious belief” model, with Bayes
factors (BF10) of 0.357 (OFC) and 0.414 (IPL), suggesting
that the data under the null model is more plausible than
under the religious belief model.
3.5.2
|
Mystical experience
Similar to the religious belief analyses, for all three ROIs
(IPL, rMTL and hippocampus) there was weak evidence for
the null model, with Bayes factors (BF10) of 0.283 (IPL),
0.357 (rMTL) and 0.328 (hippocampus), again suggesting
that the data under the null model is more plausible than
under the mystical experience model.
3.6
|
Whole‐brain analysis
In addition to the ROI analyses of religious belief and mysti-
cal experience, we also conducted a whole‐brain voxel‐wise
FIGURE 2 Whole‐brain significant (ɑ=0.025) voxel‐wise t‐statistics of the effect of gender computed with a (non‐parametric) general
linear model on the threshold‐free cluster enhancement‐transformed and thresholded voxel‐based morphometry data. Red‐yellow voxels represent
a significantly higher local gray matter volume for male than for female participants, while blue voxels represent a significantly higher local
gray matter volume for female than for male participants. Unthresholded statistical brain maps (t‐values and 1−p maps) can be viewed at and
downloaded from https ://ident ifiers.org/neuro vault.colle ction :5380. [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 3 Outline of region‐of‐interests (ROIs) used in this study (Hippoc., hippocampus; IPL, inferior parietal lobe; MTL,
mediotemporal lobe; OFC, orbitofrontal cortex). All ROIs were bilateral, except for the (right hemisphere) MTL. [Colour figure can be viewed at
wileyonlinelibrary.com]
860
|
van ELK and SnOEK
analysis with religious belief and mystical experience as co-
variates (with identical settings as the outcome neutral whole‐
brain analysis). We used a significance level of 0.025 as we
conducted a two‐sided test (i.e., we tested both for positive
and negative associations of our covariates of interest with the
VBM data; cf., Chen etal., 2019). As can be seen in Figure4,
no voxels were found to be significant after multiple com-
parison correction. Unthresholded whole‐brain maps can be
found in the neurovault repository belonging to this study.
3.7
|
Exploratory results
In addition to the preregistered analyses, in an exploratory
analysis we found hippocampus gray matter volume was
positively associated with religious belief (after adjusting for
age, intelligence and gender), as indicated by a Bayes factor
(BF10) of 3.512 in favor of the model including religious be-
lief. Although this Bayes factor suggests a moderate amount
of evidence for the observed effect (Jeffreys, 1961), we stress
that the reader should interpret this effect with care as this
analysis was not preregistered. To aid the interpretation of
the strength of the effect, Figure5 shows a partial (frequen-
tist) regression plot, showing the effect of religiosity on hip-
pocampal gray matter volume after partialling out the effects
of age, intelligence and gender.
4
|
DISCUSSION
In this registered report, we investigated whether religios-
ity and mystical experiences were associated with struc-
tural brain differences in gray matter volume. By using an
FIGURE 4 Whole‐brain results of religious belief and mystical experience contrasts. After multiple comparison correct, no voxels showed
a significant difference from zero. Unthresholded statistical brain maps (t‐values and 1−p maps) can be viewed at and downloaded from https ://
ident ifiers.org/neuro vault.colle ction :5380
FIGURE 5 The regression line
describes the effect of religious belief
on hippocampal gray matter volume
after partialling out the effects of gender,
intelligence and age, indicating a Bayes
factor (BF10) of 3.512 in favor of the model
including religious belief. The partial
regression analysis was performed using the
statsmodels Python package. [Colour figure
can be viewed at wileyonlinelibrary.com]
|
861
van ELK and SnOEK
outcome neutral criterion, we were able to show the validity
of our experimental and analytical approach, by identify-
ing clear gender differences in gray matter volume between
men and women (Takahashi etal., 2011). However, we did
not observe structural brain differences in association with
self‐reported religiosity or mystical experiences, neither
using an ROI analysis, nor using a whole‐brain analysis.
Overall, we observed moderate evidence for the null model
according to which gray matter volume in the OFC, the
bilateral IPL, the rMTL and the hippocampus are best ex-
plained by gender, age and intelligence, rather than religi-
osity or mystical experiences.
These findings cast new light on the claim that religion
is hardwired in the brain. Many previous studies in the field
of the neuroscience of religion have suffered from method-
ological problems, such as the lack of experimental control,
problems with ecological validity and low statistical power
(Schjødt & van Elk, 2019). The current replication study
comprised a relatively large sample and we used well‐vali-
dated measures of religiosity and mysticism, thereby over-
coming the limitations of previous research. Based on a
thorough literature review, we also used an ROI‐based anal-
ysis, resulting in a relatively high statistical power. Still,
the outcomes were not promising: religiosity and mystical
experiences were not consistently related to gray matter
volume differences. We note that in our exploratory analy-
sis a positive correlation was found between hippocampal
gray matter volume and religiosity. This finding needs to
be interpreted with caution as it was not preregistered and
the correlation is also contrary to the effects that have been
observed in earlier studies, indicating that hippocampal at-
rophy was related to an increased religiosity, that is, a neg-
ative correlation between hippocampal gray matter volume
and religiosity (Chan etal., 2009; Owen etal., 2011). Still,
a future independent replication study could take this unex-
pected finding into account, by conducting a confirmatory
ROI analysis of this relationship.
The absence of a clear and consistent relation between
religiosity and structural brain differences may not appear
surprising in the light of the recent replication crisis that
has haunted psychology and neuroscience as well (Zwaan,
Etz, Lucas, & Donnellan, 2017). Previous replications at-
tempts have shown that correlations between structural
brain properties and behavior and personality measures in
general are notoriously difficult to replicate (Boekel etal.,
2015; Melonakos etal., 2011). The field of neuroscience is
plagued with many low‐powered studies and accordingly the
literature abounds with many false‐positive findings, result-
ing in an overall inconsistent and scattered pattern of results
(Button etal., 2013). Another problem related with identify-
ing the structural brain correlates of religiosity is that other
confounding factors tend to covary with religion, such as gen-
der, age, schizotypy but also mental and physical health (e.g.,
living a healthier lifestyle by adhering to one's religious pre-
scriptions; cf., Maltby, Garner, Lewis, & Day, 2000; Miller
& Hoffmann, 1995; Stavrova, Fetchenhauer, & Schlösser,
2013). These factors in turn also directly have an effect on
gray matter volume (Goodkind etal., 2015; Modinos etal.,
2010), thereby further obscuring an eventual effect.
On a more positive note, a promising alternative to study-
ing structural brain differences is the use of multivariate
pattern recognition (Calhoun, Lawrie, Mourao‐Miranda, &
Stephan, 2017) and network analysis techniques (Sporns,
2014). These methods provide an increased sensitivity, as-
suming that confounds are properly controlled for (Snoek,
Miletić, & Scholte, 2019), because they allow identify-
ing multidimensional spatially distributed representations,
which is beyond the reach of classic univariate approaches
(Jimura & Poldrack, 2012). Relatedly, as already outlined in
the Introduction, several functional neurocognitive mecha-
nisms have been proposed to underlie a general propensity
for religiosity and religious experiences, such as for instance
a reduced error monitoring mechanism (van Elk & Aleman,
2017). Putting these ideas to the test would require setting
up carefully designed functional neuroimaging studies. These
would need to do justice to both the requirement to study au-
thentic religious beliefs and practices, while also providing
sufficient experimental control (Schjødt & van Elk, 2019).
We note that we currently have two studies underway in
line with this approach: in one study, we assess the effects
of source credibility in believers vs. non‐believers (Schjoedt
etal., 2011), and while in the other, we assess the relationship
between neurocognitive conflict detection in a Stroop task
and religiosity (Hoogeveen, Snoek & van Elk, in prep.). An
alternative and complementary approach is to deconstruct re-
ligion in its constitutive components, such as rituals, morality
and belief in minimally counterintuitive concepts (McKay &
Whitehouse, 2015). Each of these topics could be related to
the extant literature in social and cognitive neuroscience.
5
|
CONCLUSION
In this study, we found no evidence that religiosity is as-
sociated with a reduced volume of the orbito‐frontal cortex
and changes in the structure of the bilateral inferior parietal
lobes. Neither did we find support for the notion that mys-
tical experiences are associated with a reduced volume of
the hippocampus, the right middle temporal gyrus or with
the inferior parietal lobes. A whole‐brain analysis further-
more indicated that no structural brain differences were
found in association with religiosity and mystical experi-
ences. The search for the neural correlates of religious be-
liefs and experiences should therefore probably shift focus
from studying structural brain differences, to a functional
and multivariate approach.
862
|
van ELK and SnOEK
ACKNOWLEDGEMENTS
This study was supported by a grant from the John Templeton
Foundation (grant # 60663).
CONFLICT OF INTEREST
The authors declare to have no conflict of interest.
AUTHOR CONTRIBUTIONS
MvE designed the study; MvE & LS wrote the RR; LS super-
vised data collection; LS analyzed the data with input from
MvE.
DATA AVAILABILITY STATEMENT
All analysis code and code to reproduce the figures of this
manuscript are available from the project's GitHub reposi-
tory: https ://github.com/lukas snoek/ Relig iosit yVBM. This
repository also contains a csv‐file with the data to reproduce
the ROI analyses (i.e., the ROI‐average gray matter volume,
nuisance variables and religious belief/mystical experience
variables). Unthresholded brain maps from the whole‐brain
analysis of both the outcome neutral test and main analysis
can be viewed and downloaded from this project's Neurovault
repository: https ://ident ifiers.org/neuro vault.colle ction :5380.
The project was preregistered on the open‐science frame-
work (OSF) at https ://osf.io/qzkmh/ .
ORCID
Michiel van Elk https://orcid.org/0000-0002-7631-3551
REFERENCES
Andersson, J. L., Jenkinson, M., & Smith, S. (2007). Non‐linear registra-
tion, aka spatial normalisation. In FMRIB technical report TR07JA2
(Vol. 2, pp. 1–21). FMRIB Analysis Group of the University of
Oxford.
Argue, A., Johnson, D. R., & White, L. K. (1999). Age and religiosity:
Evidence from a three‐wave panel analysis. Journal for the Scientific
Study of Religion, 38(3), 423–435.
Arzy, S., & Schurr, R. (2016)“God has sent me to you”: Right temporal
epilepsy, left prefrontal psychosis. Epilepsy and Behavior, 60, 7–10.
Ashburner, J., & Friston, K. J. (2000). Voxel‐based morphometry—The
methods. NeuroImage, 11(6), 805–821.
Beauregard, M., & O'Leary, D. (2007). The spiritual brain. New York,
NY: Harper Collins.
Blanke, O. (2012). Multisensory brain mechanisms of bodily self‐con-
sciousness. Nature Reviews Neuroscience, 13(8), 556–571. https ://
doi.org/10.1038/nrn3292
Blanke, O., Slater, M., & Serino, A. (2015). Behavioral, neural, and
computational principles of bodily self‐consciousness. Neuron,
88(1), 145–166.
Boekel, W., Wagenmakers, E. J., Belay, L., Verhagen, J., Brown, S., &
Forstmann, B. U. (2015). A purely confirmatory replication study of
structural brain‐behavior correlations. Cortex, 66, 115–133.
Boyer, P. (2003). Religious thought and behaviour as by‐products of
brain function. Trends in Cognitive Sciences, 7(3), 119–134.
Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph the-
oretical analysis of structural and functional systems. Nature Reviews
Neuroscience, 10(4), 186–198. https ://doi.org/10.1038/nrn2618
Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J.,
Robinson, E. S., & Munafò, M. R. (2013). Power failure: Why small
sample size undermines the reliability of neuroscience. Nature
Reviews Neuroscience, 14(5), 1–12.
Calhoun, V. D., Lawrie, S. M., Mourao‐Miranda, J., & Stephan, K. E.
(2017). Prediction of individual differences from neuroimaging
data. NeuroImage, 145(Pt B), 135–136.
Chan, D., Anderson, V., Pijnenburg, Y., Whitwell, J., Barnes, J., Scahill,
R., & Fox, N. C. (2009). The clinical profile of right temporal lobe
atrophy. Brain, 132, 1287–1298. https ://doi.org/10.1093/brain/
awp037
Chen, G., Cox, R. W., Glen, D. R., Rajendra, J. K., Reynolds, R. C.,
& Taylor, P. A. (2019). A tail of two sides: Artificially doubled
false positive rates in neuroimaging due to the sidedness choice
with t‐tests. Human Brain Mapping, 40(3), 1037–1043. https ://doi.
org/10.1002/hbm.24399
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1),
155–159.
Costa, P. T., & MacCrae, R. R. (1992). Revised NEO personality in-
ventory (NEO PI‐R) and NEO five‐factor inventory (NEO‐FFI):
Professional manual. Odessa, FL: Psychological Assessment
Resources, Incorporated.
Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., &
Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spa-
tially constrained spectral clustering. Human Brain Mapping, 33(8),
1914–1928.
Cremers, H. R., Wager, T. D., & Yarkoni, T. (2017). The relation be-
tween statistical power and inference in fMRI. PLoS One, 12(11),
e0184923.
Cristofori, I., Bulbulia, J., Shaver, J. H., Wilson, M., Krueger, F., &
Grafman, J. (2016). Neural correlates of mystical experience.
Neuropsychologia, 80, 212–220. https ://doi.org/10.1016/j.neuro
psych ologia.2015.11.021
Devinsky, O., & Lai, G. (2008). Spirituality and religion in epilepsy.
Epilepsy and Behavior, 12(4), 636–643. https ://doi.org/10.1016/j.
yebeh.2007.11.011
Douaud, G., Smith, S., Jenkinson, M., Behrens, T., Johansen‐Berg,
H., Vickers, J., & James, A. (2007). Anatomically related grey
and white matter abnormalities in adolescent‐onset schizophrenia.
Brain, 130(9), 2375–2386.
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A.,
& Gorgolewski, K. J. (2017a). MRIQC: Advancing the automatic
prediction of image quality in MRI from unseen sites. PloS One,
12(9), e0184661.
Esteban, O., Gorgolewski, K., Blair, R., Berleant, S., Moodie, C., &
Poldrack, R. A. (2017b). FMRIprep: A robust preprocessing pipe-
line for task‐based and resting‐state fMRI data. Paper presented at
the Annual Meeting of the Organization for Human Brain Mapping.
Freese, J. (2004). Risk preferences and gender differences in religious-
ness: Evidence from the World Values Survey. Review of Religious
Research, 46(1), 88–91. https ://doi.org/10.2307/3512255
|
863
van ELK and SnOEK
Good, C. D., Johnsrude, I., Ashburner, J., Henson, R. N. A., Friston, K.
J., & Frackowiak, R. S. J. (2001). Cerebral asymmetry and the ef-
fects of sex and handedness on brain structure: A voxel‐based mor-
phometric analysis of 465 normal adult human brains. NeuroImage,
14(3), 685–700. https ://doi.org/10.1006/nimg.2001.0857
Goodkind, M., Eickhoff, S. B., Oathes, D. J., Jiang, Y., Chang, A., Jones‐
Hagata, L. B., & Grieve, S. M. (2015). Identification of a common
neurobiological substrate for mental illness. JAMA Psychiatry,
72(4), 305–315.
Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y.
O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: A flexible,
lightweight and extensible neuroimaging data processing framework
in python. Frontiers in Neuroinformatics, 5(13), 1–15.
Grill‐Spector, K., & Malach, R. (2004). The human visual cortex. Annual
Review of Neuroscience, 27, 649–677. https ://doi.org/10.1146/annur
ev.neuro.27.070203.144220
Hariri, A. R., Bookheimer, S. Y., & Mazziotta, J. C. (2000). Modulating
emotional responses: Effects of a neocortical network on the limbic
system. NeuroReport, 11(1), 43–48.
Hayward, R. D., Owen, A. D., Koenig, H. G., Steffens, D. C., & Payne,
M. E. (2011). Associations of religious behavior and experiences
with extent of regional atrophy in the orbitofrontal cortex during
older adulthood. Religion Brain and Behavior, 1(2), 103–118. https
://doi.org/10.1080/21535 99x.2011.598328
Hood, R. W. Jr (1975). The construction and preliminary validation of a
measure of reported mystical experience. Journal for the Scientific
Study of Religion, 14(1), 29–41.
Hoogeveen, S., Snoek, L., & van, Elk, M. (in prep.). Religious belief &
cognitive control: an fMRI study.
Inzlicht, M., McGregor, I., Hirsh, J. B., & Nash, K. (2009). Neural mark-
ers of religious conviction. Psychological Science, 20(3), 385–392.
https ://doi.org/10.1111/j.1467-9280.2009.02305.x
Inzlicht, M., & Tullett, A. M. (2010). Reflecting on god: Religious primes
can reduce neurophysiological response to errors. Psychological
Science, 21(8), 1184–1190. https ://doi.org/10.1177/09567 97610
375451
Jeffreys, H. (1961). Theory of probability. Oxford, UK: Oxford
University Press.
Jenkinson, M. (2003). Fast, automated, N‐dimensional phase‐unwrap-
ping algorithm. Magnetic Resonance in Medicine, 49(1), 193–197.
Jimura, K., & Poldrack, R. A. (2012). Analyses of regional‐average
activation and multivoxel pattern information tell complementary
stories. Neuropsychologia, 50(4), 544–552.
Johnstone, B., Bayan, S., Gutierrez, L., Lardizabal, D., Lanigar, S.,
Yoon, D. P., & Judd, K. (2014). Neuropsychological correlates of
forgiveness. Religion, Brain and Behavior, 5(1), 24–35.
Johnstone, B., Bodling, A., Cohen, D., Christ, S. E., & Wegrzyn, A.
(2012). Right parietal lobe‐related “selflessness” as the neuropsy-
chological basis of spiritual transcendence. International Journal
for the Psychology of Religion, 22(4), 267–284. https ://doi.
org/10.1080/10508 619.2012.657524
Johnstone, B., & Glass, B. A. (2008). Support for a neuropsychological
model of spirituality in persons with traumatic brain injury. Zygon,
43(4), 861–874. https ://doi.org/10.1111/j.1467-9744.2008.00964.x
Jones, O. P., Alfaro‐Almagro, F., & Jbabdi, S. (2018). An empirical,
21st century evaluation of phrenology. bioRxiv, 243089.
Joseph, R. (2001). The limbic system and the soul: Evolution and the
neuroanatomy of religious experience. Zygon, 36(1), 105–136. https
://doi.org/10.1111/0591-2385.00343
Kaasinen, V., Maguire, R. P., Kurki, T., Bruck, A., & Rinne, J. O.
(2005). Mapping brain structure and personality in late adult-
hood. NeuroImage, 24(2), 315–322. https ://doi.org/10.1016/j.neuro
image.2004.09.017
Kapogiannis, D., Barbey, A. K., Su, M., Krueger, F., & Grafman, J.
(2009). Neuroanatomical variability of religiosity. PLoS One, 4(9),
e7180. https ://doi.org/10.1371/journ al.pone.0007180
Kapogiannis, D., Barbey, A. K., Su, M., Zamboni, G., Krueger, F.,
& Grafman, J. (2009). Cognitive and neural foundations of reli-
gious belief. Proceedings of the National Academy of Sciences
of the United States of America, 106(12), 4876–4881. https ://doi.
org/10.1073/pnas.08117 17106
Lanman, J. A., & Buhrmester, M. D. (2017). Religious actions speak
louder than words: Exposure to credibility‐enhancing displays pre-
dicts theism. Religion, Brain and Behavior, 7(1), 3–16.
Lindeman, M., Svedholm, A. M., Riekki, T., Raij, T., & Hari, R.
(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
Love, J., Selker, R., Marsman, M., Jamil, T., Dropmann, D., Verhagen,
A. J., … Wagenmakers, E.‐J. (2015). JASP (Version 0.7.5).
Luhrmann, T. M. (2011). Hallucinations and sensory overrides. Annual
Review of Anthropology, 40, 71–85.
Luhrmann, T. M., Nusbaum, H., & Thisted, R. (2013). “Lord, teach us
to pray”: Prayer practice affects cognitive processing. Journal of
Cognition and Culture, 13(1–2), 159–177.
Maij, D. L., & van Elk, M. (2018). Getting absorbed in experimen-
tally induced extraordinary experiences: Effects of placebo brain
stimulation on agency detection. Consciousness and Cognition, 66,
1–16.
Maij, D. L., van Elk, M., & Schjoedt, U. (2017). The role of alcohol
in expectancy‐driven mystical experiences: A pre‐registered field
study using placebo brain stimulation. Religion, Brain and Behavior,
9(2), 108–125.
Maij, D. L., van Harreveld, F., Gervais, W., Schrag, Y., Mohr, C., &
van Elk, M. (2017). Mentalizing skills do not differentiate believ-
ers from non‐believers, but credibility enhancing displays do. PLoS
One, 12(8), e0182764.
Maltby, J., Garner, I., Lewis, C. A., & Day, L. (2000). Religious orien-
tation and schizotypal traits. Personality and Individual Differences,
28(1), 143–151.
Marsman, M., & Wagenmakers, E. J. (2017). Bayesian benefits with
JASP. European Journal of Developmental Psychology, 14(5),
545–555.
McKay, R., & Whitehouse, H. (2015). Religion and morality.
Psychological Bulletin, 141(2), 447–473.
Melonakos, E. D., Shenton, M. E., Rathi, Y., Terry, D. P., Bouix, S., &
Kubicki, M. (2011). Voxel‐based morphometry (VBM) studies in
schizophrenia—Can white matter changes be reliably detected with
VBM? Psychiatry Research: Neuroimaging, 193(2), 65–70.
Miller, L., Bansal, R., Wickramaratne, P., Hao, X. J., Tenke, C. E.,
Weissman, M. M., & Peterson, B. S. (2014). Neuroanatomical cor-
relates of religiosity and spirituality a study in adults at high and low
familial risk for depression. JAMA Psychiatry, 71(2), 128–135. https
://doi.org/10.1001/jamap sychi atry.2013.3067
Miller, A. S., & Hoffmann, J. P. (1995). Risk and religion – An expla-
nation of gender differences in religiosity. Journal for the Scientific
Study of Religion, 34(1), 63–75. https ://doi.org/10.2307/1386523
864
|
van ELK and SnOEK
Miller, B. L., Mychack, P., Seeley, W. W., Rosen, H. J., & Boone, K.
(2001). Neuroanatomy of the self: Evidence from patients with fron-
totemporal dementia. Neurology, 56(8), A144.
Modinos, G., Mechelli, A., Ormel, J., Groenewold, N. A., Aleman,
A., & McGuire, P. K. (2010). Schizotypy and brain structure: A
voxel‐based morphometry study. Psychological Medicine, 40(9),
1423–1431.
Morey, R. D., & Rouder, J. N. (2015). BayesFactor (Version 0.9.11‐3).
Newberg, A., Alavi, A., Baime, M., Pourdehnad, M., Santanna, J., &
d'Aquid, E. (2001). The measurement of regional cerebral blood
flow during the complex cognitive task of meditation: A prelim-
inary SPECT study. Psychiatry Research‐Neuroimaging, 106(2),
113–122. https ://doi.org/10.1016/s0925-4927(01)00074-9
Newberg, A., d'Aquili, E. G., & Rause, V. (2001). Why God won't go
away: Brain science and the biology of belief. New York, NY:
Ballantine Books.
Newberg, A., & Iversen, J. (2003). The neural basis of the complex
mental task of meditation: Neurotransmitter and neurochemical
considerations. Medical Hypotheses, 61(2), 282–291. https ://doi.
org/10.1016/s0306-9877(03)00175-0
Oosterwijk, S. (2017). Choosing the negative: A behavioral demonstra-
tion of morbid curiosity. PLoS One, 12(7), e0178399.
Owen, A. D., Hayward, R. D., Koenig, H. G., Steffens, D. C., &
Payne, M. E. (2011). Religious factors and hippocampal atrophy
in late life. PLoS One, 6(3), e17006. https ://doi.org/10.1371/journ
al.pone.0017006
Pelletier‐Baldelli, A., Dean, D. J., Lunsford‐Avery, J. R., Watts, A.
K. S., Orr, J. M., Gupta, T., & Mittal, V. A. (2014). Orbitofrontal
cortex volume and intrinsic religiosity in non‐clinical psychosis.
Psychiatry Research‐Neuroimaging, 222(3), 124–130. https ://doi.
org/10.1016/j.pscyc hresns.2014.03.010
Pessoa, L., Gutierrez, E., Bandettini, P. A., & Ungerleider, L. G. (2002).
Neural correlates of visual working memory: fMRI amplitude pre-
dicts task performance. Neuron, 35(5), 975–987.
Phillips, L. H., MacPherson, S. E., & Della Sala, S. (2002). Age, cogni-
tion and emotion: The role of anatomical segregation in the frontal
lobes: The role of anatomical segregation in the frontal lobes. In J.
Grafman (ed.), Handbook of Neuropsychology: The frontal lobes.
Amsterdam: Elsevier Science.
Piedmont, R. L. (1999). Does spirituality represent the sixth fac-
tor of personality? Spiritual transcendence and the five‐fac-
tor model. Journal of Personality, 67(6), 985–1013. https ://doi.
org/10.1111/1467-6494.00080
Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Handbook
of functional MRI data analysis. Cambridge, MA: Cambridge
University Press.
Raven, J. (2000). The Raven's progressive matrices: Change and stabil-
ity over culture and time. Cognitive Psychology, 41(1), 1–48.
Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012).
Default Bayes factors for ANOVA designs. Journal of Mathematical
Psychology, 56, 356–374.
Saver, J. L., & Rabin, J. (1997). The neural substrates of religious ex-
perience. Journal of Neuropsychiatry and Clinical Neurosciences,
9(3), 498–510.
Schjødt, U., & van Elk, M. (2019). The neuroscience of religion. In J. L.
Barrett (Ed.), Oxford handbook of the cognitive science of religion.
Oxford, UK: Oxford University Press.
Schjoedt, U. (2009). The religious brain: A general introduction to the
experimental neuroscience of religion. Method and Theory in the
Study of Religion, 21(3), 310–339. https ://doi.org/10.1163/15700
6809x 460347
Schjoedt, U., Sørensen, J., Nielbo, K. L., Xygalatas, D., Mitkidis, P., &
Bulbulia, J. (2013). Cognitive resource depletion in religious inter-
actions. Religion, Brain and Behavior, 3(1), 39–55.
Schjoedt, U., Stdkilde‐Jorgensen, H., Geertz, A. W., & Roepstorff, A.
(2009). Highly religious participants recruit areas of social cogni-
tion in personal prayer. Social Cognitive and Affective Neuroscience,
4(2), 199–207. https ://doi.org/10.1093/scan/nsn050
Schjoedt, U., Stodkilde‐Jorgensen, H., Geertz, A. W., Lund, T. E., &
Roepstorff, A. (2011). The power of charisma‐perceived charisma
inhibits the frontal executive network of believers in intercessory
prayer. Social Cognitive and Affective Neuroscience, 6(1), 119–127.
https ://doi.org/10.1093/scan/nsq023
Smith, C. D., Chebrolu, H., Wekstein, D. R., Schmitt, F. A., &
Markesbery, W. R. (2007). Age and gender effects on human brain
anatomy: A voxel‐based morphometric study in healthy elderly.
Neurobiology of Aging, 28(7), 1075–1087. https ://doi.org/10.1016/j.
neuro biola ging.2006.05.018
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F.,
Behrens, T. E., Johansen‐Berg, H., & Niazy, R. K. (2004). Advances
in functional and structural MR image analysis and implementation
as FSL. NeuroImage, 23, S208–S219.
Smith, S. M., & Nichols, T. E. (2009). Threshold‐free cluster enhance-
ment: Addressing problems of smoothing, threshold dependence
and localisation in cluster inference. NeuroImage, 44(1), 83–98.
Snoek, L., Miletic, S., & Scholte, H. S. (2018). How to control for con-
founds in decoding analyses of neuroimaging data. bioRxiv, 290684.
Snoek, L., Miletić, S., & Scholte, H. S. (2019). How to control for con-
founds in decoding analyses of neuroimaging data. NeuroImage,
184, 741–760.
Sporns, O. (2014). Contributions and challenges for network models in
cognitive neuroscience. Nature Neuroscience, 17(5), 652–660.
Stavrova, O., Fetchenhauer, D., & Schlösser, T. (2013). Why are re-
ligious people happy? The effect of the social norm of religiosity
across countries. Social Science Research, 42(1), 90–105.
Takahashi, R., Ishii, K., Kakigi, T., & Yokoyama, K. (2011). Gender and
age differences in normal adult human brain: Voxel‐based morpho-
metric study. Human Brain Mapping, 32(7), 1050–1058.
Tellegen, A., & Atkinson, G. (1974). Openness to absorbing and self‐al-
tering experiences (“absorption”), a trait related to hypnotic suscep-
tibility. Journal of Abnormal Psychology, 83(3), 268–277.
Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A.,
Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3
bias correction. IEEE Transactions on Medical Imaging, 29(6),
1310–1320.
Urgesi, C., Aglioti, S. M., Skrap, M., & Fabbro, F. (2010). The spiri-
tual brain: Selective cortical lesions modulate human self‐transcen-
dence. Neuron, 65(3), 309–319. https ://doi.org/10.1016/j.neuron
2010.01.026
Van Den Heuvel, M. P., & Pol, H. E. H. (2010). Exploring the brain
network: A review on resting‐state fMRI functional connectivity.
European Neuropsychopharmacology, 20(8), 519–534.
van Elk, M. (2015). An EEG study on the effects of induced spiritual
experiences on somatosensory processing and sensory suppression.
Journal for the Cognitive Science of Religion, 2, 121–157.
van Elk, M., & Aleman, A. (2017). Brain mechanisms in religion
and spirituality: An integrative predictive processing framework.
Neuroscience and Biobehavioral Reviews, 73, 359–378.
|
865
van ELK and SnOEK
van Elk, M., Karinen, A., Specker, E., Stamkou, E., & Baas, M. (2016).
‘Standing in Awe’: The effects of awe on body perception and the
relation with absorption. Collabra, 2(1), 2–16.
van Elk, M., Rutjens, B. T., van der Pligt, J., & Van Harreveld, F. (2016).
Priming of supernatural agent concepts and agency detection.
Religion, Brain and Behavior, 6(1), 4–33.
van Elk, M., Rutjens, B. T., & van Harreveld, F. (2017). Why are
protestants more prosocial than Catholics? A comparative study
among orthodox Dutch believers. The International Journal for the
Psychology of Religion, 27(1), 65–81.
Van Schuerbeek, P., Baeken, C., De Raedt, R., De Mey, J., & Luypaert,
R. (2011). Individual differences in local gray and white matter
volumes reflect differences in temperament and character: A voxel‐
based morphometry study in healthy young females. Brain Research,
1371, 32–42. https ://doi.org/10.1016/j.brainres 2010.11.073
Wuerfel, J., Krishnamoorthy, E. S., Brown, R. J., Lemieux, L., Koepp,
M., Van Elst, L. T., & Trimble, M. R. (2004). Religiosity is as-
sociated with hippocampal but not amygdala volumes in patients
with refractory epilepsy. Journal of Neurology, Neurosurgery and
Psychiatry, 75(4), 640–642.
Zhong, W., Cristofori, I., Bulbulia, J., Krueger, F., & Grafman, J. (2017).
Biological and cognitive underpinnings of religious fundamental-
ism. Neuropsychologia, 100, 18–25.
Zuckerman, M., Silberman, J., & Hall, J. A. (2013). The relation be-
tween intelligence and religiosity: A meta‐analysis and some pro-
posed explanations. Personality and Social Psychology Review,
17(4), 325–354.
Zwaan, R. A., Etz, A., Lucas, R. E., & Donnellan, M. B. (2017). Making
replication mainstream. Behavioral and Brain Sciences, 41, 1–50.
https ://doi.org/10.1017/s0140 525x1 7001972
How to cite this article: van Elk M, Snoek L. The
relationship between individual differences in gray
matter volume and religiosity and mystical
experiences: A preregistered voxel‐based
morphometry study. Eur J Neurosci. 2020;51:850–
865. https ://doi.org/10.1111/ejn.14563
... With this in mind, we have run several large-scale "population imaging" MRI projects over the past decade at the University of Amsterdam, with the aim to reliably estimate the (absence of) structural and functional correlates of human behavior and mental processes. After publishing several articles using these datasets [4][5][6][7][8] , we believe that making the data from these projects publicly available will benefit the neuroimaging community most. To this end, we present the Amsterdam Open MRI Collection (AOMIC) -three large-scale datasets with high-quality, multimodal 3 T MRI data and detailed demographic and psychometric data, which are publicly available from the OpenNeuro data sharing platform. ...
Article
Full-text available
We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.
... A link can be made here to how many deconvert from religion around adolescence, which is when this part of the brain develops further and doubt processing increases (Asp & Tranel, 2013). However, despite parts of the brain being associated with experiences that individuals view as religious or mystical experiences (Cristofori et al., 2016), no relationship has been found between the volume or changed volume of any areas of the brain and mystical experiences (van Elk & Snoek, 2020). ...
Chapter
Full-text available
This chapter reviews and extends current evolutionary perspectives on atheism.
... Our religiosity measure consisted of 7 items that were based on religiosity ques- (Lindeman et al., 2015;Norenzayan et al., 2012;Stavrova, 2015) and the items have been used in previous studies (Maij et al., 2017;van Elk and Snoek, 2019). The items were evaluated on a 5-point Likert scale ranging from not at all to very much; see Table 1 for the exact items. ...
Article
Full-text available
In the current preregistered fMRI study, we investigated the relationship between religiosity and behavioral and neural mechanisms of conflict processing, as a conceptual replication of the study by Inzlicht et al. (2009). Participants (N=193) performed a gender-Stroop task and afterwards completed standardized measures to assess their religiosity. As expected, the task induced cognitive conflict at the behavioral level and at a neural level this was reflected in increased activity in the anterior cingulate cortex (ACC). However, individual differences in religiosity were not related to performance on the Stroop task as measured in accuracy and interference effects, nor to neural markers of response conflict (correct responses vs. errors) or informational conflict (congruent vs. incongruent stimuli). Overall, we obtained moderate to strong evidence in favor of the null hypotheses that religiosity is unrelated to cognitive conflict sensitivity. We discuss the implications for the neuroscience of religion and emphasize the importance of designing studies that more directly implicate religious concepts and behaviors in an ecologically valid manner.
... Most of the articles reviewed were based on research studies conducted in countries in Europe or North America with participants who were steeped in the JudeoChristian tradition. Most of these studies contained small sample sizes, were not preregistered, and had a questionable lack of appropriate control subjects (for a comprehensive review of these issues, see van Elk & Aleman, 2017, andvan Elk & Snoek, 2019). In addition, in certain studies, believers and nonbelievers may have similar profiles of brain activation when judging the relevance or validity of religious beliefs (e.g., Kapogi- annis et al., 2009), suggesting that differences between believers and nonbelievers are more likely to occur when researchers study other features of religious belief, such as emotional commitment, disagreement or conflict with statements threatening religious belief, or the attainment of an altered state of spiritual consciousness associated with the religious belief. ...
Article
Religion’s neural underpinnings have long been a topic of speculation and debate, but an emerging neuroscience of religion is beginning to clarify which regions of the brain integrate moral, ritual, and supernatural religious beliefs with functionally adaptive responses. Here, we review evidence indicating that religious cognition involves a complex interplay among the brain regions underpinning cognitive control, social reasoning, social motivations, and ideological beliefs.
... Our religiosity measure consisted of 7 items that were based on religiosity questions included in the World Values Survey (WVS; World Values Survey, 2010), covering religious identification, beliefs, values, and behaviors (institutionalized such as church attendance and private such as prayer). Besides having high face-validity, these measures have been validated in other studies (Lindeman, Svedholm-Hakkinen, & Lipsanen, 2015;Norenzayan, Gervais, & Trzesniewski, 2012;Stavrova, 2015) and the items have been used in previous studies (Maij et al., 2017;van Elk & Snoek, 2019). The items were evaluated on a 5-point Likert scale ranging from 1 = not at all to 5 = very much; see Table 1 for the exact items. ...
Preprint
Full-text available
In the current preregistered fMRI study, we investigated the relationship between religiosity and behavioral and neural mechanisms of conflict processing, as a conceptual replication of the study by Inzlicht et al. (2009). Participants (N = 193) performed a gender-Stroop task and afterwards completed standardized measures to assess their religiosity. As expected, the task induced cognitive conflict at the behavioral level and at a neural level this was reflected in increased activity in the anterior cingulate cortex (ACC). However, individual differences in religiosity were not related to performance on the Stroop task as measured in accuracy and interference effects, nor to neural markers of response conflict (correct responses vs. errors) or informational conflict (congruent vs. incongruent stimuli). Overall, we obtained moderate to strong evidence in favor of the null hypotheses that religiosity is unrelated to cognitive conflict sensitivity. We discuss the implications for the neuroscience of religion and emphasize the importance of designing studies that more directly implicate religious concepts and behaviors in an ecologically valid manner.
... Following the inprinciple-acceptance (IPA) stage, the data can be collected and the paper will be published irrespective of the outcomes. By using this format we recently published RRs on the (lack of) effects of religiosity on brain volume (van Elk & Snoek, 2019), and on the (lack of an) effect of a control threat manipulation on belief in God (Hoogeveen, Wagenmakers, Kay, & van Elk, 2019). For this special issue also an RR is underway. ...
Article
Full-text available
We prospectively investigate neural protective benefits against depression of cortical thickness across nine regions of a Ventral Frontotemporal Network (VFTN), previously associated with spiritual experience. Seventy-two participants at high and low risk for depression (Mean age 41 years; 22-63 years; 40 high risk, 32 low risk) were drawn from a three-generation, forty-year study. FreeSurfer estimated cortical thickness over anatomical MRIs of the brain (Year 30) for each of the nine ROIs. Depression (MDD with SAD-L; symptoms with PHQ; Years 30 and 40) and spirituality (self-report on five phenotypes; Year 35), respectively, were associated with the weighted average of nine ROIs. VFTN thickness was: 1) positively associated (p<.01) with two of five phenotypes, altruism and love of neighbor, interconnectedness at a trend level, but neither commitment nor practice, 2) inversely associated with a diagnosis of MDD (SADS-L Year 30, for any MDD in past ten years), and 3) prospectively neuroprotective against depressive symptoms (PHQ-9 Year 40) for those at high familial risk.
Article
Full-text available
Neurotheology is an emerging academic discipline that examines mind-brain relationships in terms of the inter-relatedness of neuroscience, spirituality, and religion. Neurotheology originated from brain-scan studies that revealed specific correlations between certain religious thoughts and localized activated brain areas known as “God Spots.” This relatively young scholarly discipline lacks clear consensus on its definition, ideology, purpose, or prospects for future research. Of special interest is the consideration of the next steps using brain scans to develop this field of research. This review proposes nine categories of future research that could build on the foundation laid by the prior discoveries of God Spots. Specifically, this analysis identifies some sparsely addressed issues that could be usefully explored with new kinds of brain-scan studies: neural network operations, the cognitive neuroscience of prayer, biology of belief, measures of religiosity, role of the self, learning and memory, religious and secular cognitive commonalities, static and functional anatomy, and recruitment of neural processing circuitry. God Spot research is poised to move beyond observation to robust hypothesis generation and testing.
Preprint
Full-text available
We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the Openneuro data sharing platform.
Article
Full-text available
Over the past decade, multivariate "decoding analyses" have become a popular alternative to traditional mass-univariate analyses in neuroimaging research. However, a fundamental limitation of using decoding analyses is that it remains ambiguous which source of information drives decoding performance, which becomes problematic when the to-be-decoded variable is confounded by variables that are not of primary interest. In this study, we use a comprehensive set of simulations as well as analyses of empirical data to evaluate two methods that were previously proposed and used to control for confounding variables in decoding analyses: post hoc counterbalancing and confound regression. In our empirical analyses, we attempt to decode gender from structural MRI data while controlling for the confound "brain size". We show that both methods introduce strong biases in decoding performance: post hoc counterbalancing leads to better performance than expected (i.e., positive bias), which we show in our simulations is due to the subsampling process that tends to remove samples that are hard to classify or would be wrongly classified; confound regression, on the other hand, leads to worse performance than expected (i.e., negative bias), even resulting in significant below chance performance in some realistic scenarios. In our simulations, we show that below chance accuracy can be predicted by the variance of the distribution of correlations between the features and the target. Importantly, we show that this negative bias disappears in both the empirical analyses and simulations when the confound regression procedure is performed in every fold of the cross-validation routine, yielding plausible (above chance) model performance. We conclude that, from the various methods tested, cross-validated confound regression is the only method that appears to appropriately control for confounds which thus can be used to gain more insight into the exact source(s) of information driving one's decoding analysis.
Article
Full-text available
Phrenology was a nineteenth century endeavour to link personality traits with scalp morphology, which has been both influential and fiercely criticised, not least because of the assumption that scalp morphology can be informative of underlying brain function. Here we test the idea empirically rather than dismissing it out of hand. Whereas nineteenth century phrenologists had access to coarse measurement tools (digital technology referring then to fingers), we were able to re-examine phrenology using 21st century methods and thousands of subjects drawn from the largest neuroimaging study to date. High-quality structural MRI was used to quantify local scalp curvature. The resulting curvature statistics were compared against lifestyle measures acquired from the same cohort of subjects, being careful to match a subset of lifestyle measures to phrenological ideas of brain organisation, in an effort to evoke the character of Victorian times. The results represent the most rigorous evaluation of phrenological claims to date.
Article
Full-text available
We explored the effects of alcohol on expectancy-driven mystical and quasi-mystical experiences by manipulating participants’ expectations. By using the so-called God Helmet suggestion, participants were led to believe that a placebo brain stimulation could elicit mystical experiences. In this pre-registered field study, we set out to test whether alcohol could increase participants’ susceptibility to the God Helmet suggestion in a large sample (N = 193) at a Dutch festival. Participants reported a wide range of extraordinary experiences associated with mysticism, including out-of-body experiences, involuntary movements, and the felt presence of invisible beings. Regression analyses revealed that self-identified spiritualism predicted extraordinary experiences, but neither objective nor subjective measures of alcohol intoxication increased participants’ susceptibility to the God Helmet. Methodological limitations that may explain the lack of an effect for alcohol are discussed, while we explore the usefulness of the God Helmet in the study of extraordinary experiences.
Article
Full-text available
Statistically underpowered studies can result in experimental failure even when all other experimental considerations have been addressed impeccably. In fMRI the combination of a large number of dependent variables, a relatively small number of observations (subjects), and a need to correct for multiple comparisons can decrease statistical power dramatically. This problem has been clearly addressed yet remains controversial—especially in regards to the expected effect sizes in fMRI, and especially for between-subjects effects such as group comparisons and brain-behavior correlations. We aimed to clarify the power problem by considering and contrasting two simulated scenarios of such possible brain-behavior correlations: weak diffuse effects and strong localized effects. Sampling from these scenarios shows that, particularly in the weak diffuse scenario, common sample sizes (n = 20–30) display extremely low statistical power, poorly represent the actual effects in the full sample, and show large variation on subsequent replications. Empirical data from the Human Connectome Project resembles the weak diffuse scenario much more than the localized strong scenario, which underscores the extent of the power problem for many studies. Possible solutions to the power problem include increasing the sample size, using less stringent thresholds, or focusing on a region-of-interest. However, these approaches are not always feasible and some have major drawbacks. The most prominent solutions that may help address the power problem include model-based (multivariate) prediction methods and meta-analyses with related synthesis-oriented approaches.
Article
Full-text available
The ability to mentalize has been marked as an important cognitive mechanism enabling belief in supernatural agents. In five studies we cross-culturally investigated the relationship between mentalizing and belief in supernatural agents with large sample sizes (over 67,000 participants in total) and different operationalizations of mentalizing. The relative importance of mentalizing for endorsing supernatural beliefs was directly compared with credibility enhancing displays–the extent to which people observed credible religious acts during their upbringing. We also compared autistic with neurotypical adolescents. The empathy quotient and the autism-spectrum quotient were not predictive of belief in supernatural agents in all countries (i.e., The Netherlands, Switzerland and the United States), although we did observe a curvilinear effect in the United States. We further observed a strong influence of credibility enhancing displays on belief in supernatural agents. These findings highlight the importance of cultural learning for acquiring supernatural beliefs and ask for reconsiderations of the importance of mentalizing.
Article
Full-text available
This paper examined, with a behavioral paradigm, to what extent people choose to view stimuli that portray death, violence or harm. Based on briefly presented visual cues, participants made choices between highly arousing, negative images and positive or negative alternatives. The negative images displayed social scenes that involved death, violence or harm (e.g., war scene), or decontextualized, close-ups of physical harm (e.g., mutilated face) or natural threat (e.g., attacking shark). The results demonstrated that social negative images were chosen significantly more often than other negative categories. Furthermore, participants preferred social negative images over neutral images. Physical harm images and natural threat images were not preferred over neutral images, but were chosen in about thirty-five percent of the trials. These results were replicated across three different studies, including a study that presented verbal descriptions of images as pre-choice cues. Together, these results show that people deliberately subject themselves to negative images. With this, the present paper demonstrates a dynamic relationship between negative information and behavior and advances new insights into the phenomenon of morbid curiosity.
Article
Previous work demonstrated that placebo brain stimulation can function as an experimental tool to elicit mystical and quasi-mystical (i.e., extraordinary) experiences. However, it has not yet been investigated whether these effects result from mere sensory deprivation and individual differences in suggestibility, or whether expectancy manipulations are crucial in eliciting these effects. In this study, we showed that extraordinary experiences could be systematically manipulated by means of an expectancy manipulation using a within-subjects design, while controlling for suggestibility effects. We further observed that participants’ score on the Tellegen absorption scale, an individual difference measure reflecting people's propensity to get immersed in external stimuli or mental imagery, is related to the frequency and intensity of such experiences. Finally, we investigated the relationship between extraordinary experiences and agency detection, which has been hypothesized to be associated to supernatural beliefs and experiences. The experimental induction of extraordinary experiences did not result in increased agency detection.
Article
One‐sided t‐tests are widely used in neuroimaging data analysis. While such a test may be applicable when investigating specific regions and prior information about directionality is present, we argue here that it is often mis‐applied, with severe consequences for false positive rate (FPR) control. Conceptually, a pair of one‐sided t‐tests conducted in tandem (e.g., to test separately for both positive and negative effects), effectively amounts to a two‐sided t‐test. However, replacing the two‐sided test with a pair of one‐sided tests without multiple comparisons correction essentially doubles the intended FPR of statements made about the same study; that is, the actual family‐wise error (FWE) of results at the whole brain level would be 10% instead of the 5% intended by the researcher. Therefore, we strongly recommend that, unless otherwise explicitly justified, two‐sided t‐tests be applied instead of two simultaneous one‐sided t‐tests.
Article
Many philosophers of science and methodologists have argued that the ability to repeat studies and obtain similar results is an essential component of science. A finding is elevated from single observation to scientific evidence when the procedures that were used to obtain it can be reproduced and the finding itself can be replicated. Recent replication attempts show that some high profile results---most notably in psychology, but in many other disciplines as well---cannot be replicated consistently. These replication attempts have generated a considerable amount of controversy and the issue of whether direct replications have value has, in particular, proven to be contentious. However, much of this discussion has occurred in published commentaries and social media outlets, resulting in a fragmented discourse. To address the need for an integrative summary, we review various types of replication studies and then discuss the most commonly voiced concerns about direct replication. We provide detailed responses to these concerns and consider different statistical ways to evaluate replications. We conclude there are no theoretical or statistical obstacles to making direct replication a routine aspect of psychological science.
Article
Beliefs profoundly affect people's lives, but their cognitive and neural pathways are poorly understood. Although previous research has identified the ventromedial prefrontal cortex (vmPFC) as critical to representing religious beliefs, the means by which vmPFC enables religious belief is uncertain. We hypothesized that the vmPFC represents diverse religious beliefs and that a vmPFC lesion would be associated with religious fundamentalism, or the narrowing of religious beliefs. To test this prediction, we assessed religious adherence with a widely-used religious fundamentalism scale in a large sample of 119 patients with penetrating traumatic brain injury (pTBI). If the vmPFC is crucial to modulating diverse personal religious beliefs, we predicted that pTBI patients with lesions to the vmPFC would exhibit greater fundamentalism, and that this would be modulated by cognitive flexibility and trait openness. Instead, we found that participants with dorsolateral prefrontal cortex (dlPFC) lesions have fundamentalist beliefs similar to patients with vmPFC lesions and that the effect of a dlPFC lesion on fundamentalism was significantly mediated by decreased cognitive flexibility and openness. These findings indicate that cognitive flexibility and openness are necessary for flexible and adaptive religious commitment, and that such diversity of religious thought is dependent on dlPFC functionality.