Shared functional connectome fingerprints following ritualistic ayahuasca intake

Abstract

The knowledge that brain functional connectomes are unique and reliable has enabled behaviourally relevant inferences at a subject level. However, whether such "fingerprints" persist under altered states of consciousness is unknown. Ayahuasca is a potent serotonergic psychedelic which produces a widespread dysregulation of functional connectivity. Used communally in religious ceremonies, its shared use may highlight relevant novel interactions between mental state and functional connectome (FC) idiosyncrasy. Using 7T fMRI, we assessed resting-state static and dynamic FCs for 21 Santo Daime members after collective ayahuasca intake in an acute, within-subject study. Here, connectome fingerprinting revealed FCs showed reduced idiosyncrasy, accompanied by a spatiotemporal reallocation of keypoint edges. Importantly, we show that interindividual differences in higher-order FC motifs are relevant to experiential phenotypes, given that they can predict perceptual drug effects. Collectively , our findings offer an example of how individualised connectivity markers can be used to trace a subjec-t's FC across altered states of consciousness.

Full text

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NeuroImage xxx (xxxx) 120480
Contents lists available at ScienceDirect
NeuroImage
journal homepage: www.elsevier.com/locate/ynimg
Shared functional connectome fingerprints following ritualistic ayahuasca
intake
Pablo Mallaroni a,⁎, Natasha L. Mason a, Lilian Kloft a, Johannes T. Reckweg a, Kim van Oorsouw b,
Stefan W. Toennes c, Hanna M. Tolle d, Enrico Amico d, Johannes G. Ramaekers a
aDepartment of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the
Netherlands
bDepartment of Forensic Psychology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
cInstitute of Legal Medicine, University Hospital, Goethe University, Frankfurt/Main, Germany
dNeuro-X Institute, EPFL, Geneva, Switzerland
ARTICLE INFO
Keywords:
Psychedelics,5-HT2A
Ayahuasca
Individual differences
Connectome fingerprints
fMRI
ABSTRACT
The knowledge that brain functional connectomes are unique and reliable has enabled behaviourally relevant in-
ferences at a subject level. However, whether such “fingerprints”persist under altered states of consciousness is
unknown. Ayahuasca is a potent serotonergic psychedelic which produces a widespread dysregulation of func-
tional connectivity. Used communally in religious ceremonies, its shared use may highlight relevant novel inter-
actions between mental state and functional connectome (FC) idiosyncrasy. Using 7T fMRI, we assessed resting-
state static and dynamic FCs for 21 Santo Daime members after collective ayahuasca intake in an acute, within-
subject study. Here, connectome fingerprinting revealed FCs showed reduced idiosyncrasy, accompanied by a
spatiotemporal reallocation of keypoint edges. Importantly, we show that interindividual differences in higher-
order FC motifs are relevant to experiential phenotypes, given that they can predict perceptual drug effects. Col-
lectively, our findings offer an example of how individualised connectivity markers can be used to trace a subjec-
t's FC across altered states of consciousness.
1. Introduction
The uniqueness of one's brain connectivity profile is being increas-
ingly recognised as a ubiquitous principle of connectomics. Akin to the
ridges and furrows that comprise our fingerprints, functional connectiv-
ity patterns derived from functional resonance magnetic imaging
(fMRI) data, also known as functional connectomes (FCs) (Smith et al.,
2013), have been found to be stable across a lifetime (Horien et al.,
2019) and hold explanatory power for robust inferences at a single-
subject level (Shen et al., 2017). Evidence has shown complex behav-
ioural phenotypes such as cognition (Sripada et al., 2020), demograph-
ics (Nielsen et al., 2019), traits such as fluid intelligence (Li et al., 2020)
or personality (Dubois et al., 2018), and even clinical outcomes
(Abdallah et al., 2021) can be reliably predicted from FCs alone. This
observation has led to calls to move away from group-level inferences
and towards interindividual differences prior to concluding on the gen-
eralisability of brain activity (Seghier and Price, 2018;Dubois and
Adolphs, 2016).
In recent years, efforts have been underway to develop the field of
“brain fingerprinting”(Amico and Goñi, 2018). First exemplified by
Finn et al., individual subjects were shown to be readily distinguishable
from a set of FCs based on their correspondence (Finn et al., 2015).
Since then, work has demonstrated that an individual's connectome fin-
gerprint across sessions can be separated into signalling motifs reflect-
ing both trait intra-subject and state-dependent inter-subject variance
(E. Sareen et al., 2021;Geerligs et al., 2015), reproducible across
modalities (Elliott et al., 2019;Vanderwal et al., 2017), acquisition
methods (de Souza Rodrigues et al., 2019;Hakim et al., 2021;Wu et al.,
2022) and durations (Amico and Goñi, 2018;Airan et al., 2016;da Silva
Castanheira et al., 2021) . These findings have contributed to the notion
that, across mental states, there exists an “intrinsic'' functional network
architecture which is inherent to brain function and exhibits subtle
variations among individuals (Fox and Raichle, 2007;Vincent et al.,
2007;Gratton et al., 2018). That said, it is important to consider that
these fingerprints of brain organisation might not just be limited to the
spatial organisation and independence of FC traits but likely also to
⁎Corresponding authors.
E-mail address: p.mallaroni@maastrichtuniversity.nl (P. Mallaroni).
https://doi.org/10.1016/j.neuroimage.2023.120480
Received in revised form 6 November 2023; Accepted 29 November 2023
1053-8119/© 20XX
Note: Low-resolution images were used to create this PDF. The original images will be used in the final composition.

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P. Mallaroni et al. NeuroImage xxx (xxxx) 120480
their temporal quality (Van De Ville et al., 2021). Spatiotemporal dy-
namics have been suggested to provide a "common currency" for mental
and neuronal states (Northoff et al., 2020), with neural processing be-
ing organised across timescales and increasing along the cortical hierar-
chy of information processing (Raut et al., 2020). According to this
view, shorter timescales in sensory areas facilitate the rapid detection
and encoding of dynamic stimuli, which are subsequently integrated by
the slower dynamics of associative areas over longer timeframes
(Hasson et al., 2008;Golesorkhi et al., 2021).
Much work has been concerned with understanding how such inher-
ent connectivity might be differentially altered according to a particu-
lar individual or mental state (Elliott et al., 2019;Gratton et al., 2018;
Porter et al., 2022). However, there is little evidence bridging these
lines of research, particularly quantifying the variance associated with
a subject versus the brain state under which it is examined (Finn et al.,
2017). Compelling evidence reveals agonism of the 5-HT2A receptor by
serotonergic psychedelics holds a central role in shaping altered states
of consciousness (ASCs) (Vollenweider and Preller, 2020) and thus, po-
tentially connectome fingerprints. Whole-brain modelling has impli-
cated 5-HT2A receptor distribution in shaping brain dynamics
(Singleton et al., 2022) whereas its stimulation enhances the temporal
diversity of brain activity (Herzog et al., 2023). These effects yield
downstream shifts in functional coupling between large-scale networks,
ultimately diminishing integrative processing across major brain net-
works (M. K. Doss et al., 2021). Within a hierarchical predictive pro-
cessing framework, these outcomes are hypothesised to be linked to the
induced subjective experience via the decreased confidence in priors
encoded by functional hierarchies (Carhart-Harris and Friston, 2019).
Accordingly, it may therefore be the case that stimulation of 5-HT2A re-
ceptors could perturb behaviourally relevant brain fingerprints other-
wise residing within high-order functional networks (Mantwill et al.,
2022). Indeed, classical psychedelics have been speculated to be poten-
tial therapeutic interventions by improving symptomatology through
rebalancing aberrant brain states (Carhart-Harris and Friston, 2019;M.
K. Doss et al., 2021;Daws et al., 2022). To date however, it is unknown
how different classical psychedelics might alter connectome finger-
prints. Subject-level analyses as devised by fingerprinting may there-
fore prove to be best-suited for modelling the neurobiology of 5-HT2A
agonists, given their highly heterogenous subjective experiences,
plasma drug concentrations, and divergent effects on resting-state net-
work organisation (Moujaes et al., 2023).
A relevant practice that is purported to achieve a communal ASC is
the ritualistic use of the psychedelic brew ayahuasca. Devised from a
combination of two different plant sources, the vine Banisteriopsis caapi
and Psychotria viridis, ayahuasca produces a profound change to subjec-
tive experience, comprising a diffuse state of cognition alongside com-
plex changes to self-referential awareness, perception, and mood (Riba
et al., 2001). Whereas Psychotria viridis is a rich source of the potent 5-
HT2A agonist N,N-dimethyltryptamine (DMT), Banisteriopsis caapi con-
tains monoamine oxidase inhibitor (MAOi) β-carbolines such as
harmine, harmaline, and tetrahydroharmine, serving to promote the
bioavailability of DMT (Riba et al., 2003). Historically, ayahuasca is
used by syncretic religions such as Santo Daime to achieve personal in-
sight, intimacy and spiritual development (Lowell and Adams, 2017)
Members of the congregation drink ayahuasca (termed Daime) commu-
nally in a ceremony referred to as the “works”(trabalhos). These are col-
lective endeavours performed by members of the congregation consist-
ing of alternating periods of song, dance, and attentive silence. Provid-
ing a formalised type of set and setting, members follow a prescribed
mental state with which to engage their symbolic and religious frame-
work (doctrina) (Hartogsohn, 2021). This ritualistic use of ayahuasca
might therefore provide a useful means by which to investigate the dis-
similarity between trait and state FC under conditions in which an indi-
vidual transitions from a normal, waking state of consciousness to a
shared altered state.
Here, we sought to understand how the inherency of a subject's FC
might alter under the altered state of consciousness induced by the ritu-
alistic consumption of ayahuasca brew. Replicating a brain fingerprint
framework (Van De Ville et al., 2021) in Santo Daime members, we
characterised changes to both static and dynamic connectome identifia-
bility at peak drug effects. Furthermore, we explored how changes to an
individual's underlying functional connectivity might subsequently
help explain aspects of their subjective experience.
2. Methods
2.1. Participants
Twenty-four volunteers were enroled in a within-subject, fixed-
order observational study. Data from three volunteers were excluded
from analyses due to excessive head motion leaving a final sample of 21
subjects (10 females) of ages 29 to 64 (M: 54.48, SD: 10.55). The cohort
consisted of experienced members of the Dutch chapter of the church of
Santo Daime. Individuals were selected based on an exclusion criterion
comprising the absence of ferromagnetic devices/implants (MRI con-
traindications), pregnancy and use of (medicinal) substances in the past
24 h Detailed demographic information pertaining to the final sample
can be found in Table S1.
All participants were fully informed of all procedures, possible ad-
verse reactions, legal rights and responsibilities, expected benefits, and
their right to voluntary termination without consequences. The study
was conducted according to the Declaration of Helsinki (1964) and
amended in Fortaleza (Brazil, October 2013) and in accordance with
the Medical Research Involving Human Subjects Act (WMO) and was
approved by the Academic Hospital and University's Medical Ethics
committee of Maastricht University (NL70901.068.19/METC19.050).
2.2. Study procedures
Participants underwent two consecutive test days; one baseline con-
dition (sober) followed by an acute condition under the influence of
ayahuasca as reported previously (Ramaekers et al., 2023) and outlined
in Fig. 1. Participants self-administered a volume of ayahuasca equiva-
lent to their usual dose (mean 24 ml, SD: 8.16), prepared from a single
batch by the Church of Santo Daime and analysed according to prior
referencing standards (see Supplementary). The brew used contained
0.14 mg/ml of DMT, 4.50 mg/ml of harmine, 0.51 mg/ml of harma-
line, and 2.10 mg/ml of tetrahydroharmine. Each self-administration
took place during a ceremony organised and supervised by the Santo
Daime church. As to facilitate the communal use of ayahuasca, partici-
pants drank the ayahuasca brew individually in the company of fellow
members while performing their collective works (singing, dancing,
meditation). Participant dosing schedules were stratified across each
lab visit with testing performed within 4 pairs of visits (6 subjects per
cycle) with each subject being tested at the same window of time as to
minimise diurnal variation. The research team was not involved in the
organisation of the ceremonies nor the production, dosing, or adminis-
tration of ayahuasca.
On each day upon arrival to the lab, the absence of drug and alcohol
use was assessed via a urine drug screen and a breath alcohol test. An
additional pregnancy test was given if the participants were female.
Each visit consisted of a 30-minute wait period, followed by a 1 h MRI
scanning session occurring 1 h after intake. On day 2, venous blood
samples were collected approximately 60 and 160 min after ayahuasca
intake to assess serum concentrations of alkaloids according to labora-
tory protocols (see Supplementary). The retrospective 5-Dimensions of
Altered States of Consciousness (5D-ASC) scale (Studerus et al., 2010)
and the Ego Dissolution Inventory (Nour et al., 2016) were adminis-
tered 360 min after drug ingestion to assess the subjective experience
after drug intake. Following study completion, each subject was con-
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P. Mallaroni et al. NeuroImage xxx (xxxx) 120480
Fig. 1. Testing day schedule. Overview of each visit to the lab. 4 groups of 6 Santo Daime members visited the lab on two occasions. Acute dosing days comprised
of successive self-administrations in the presence of other group members. Participants were then sequentially scanned after their respective intake, with testing
timelines following the same schedule as the preceding baseline visit to minimise diurnal variation. Acute dosing visits comprised additional serum pharmacoki-
netic measurements at +60 min and +160 min. Retrospective questionnaires (Ego Dissolution Inventory [EDI], 5-Dimensions of Altered States Questionnaire [5D-
ASC]) were administered at the end of each acute visit.
tacted for an online follow-up (+ 6 months). In order to gauge the
prevalence of mental processes pertaining to the ceremonial use of
Daime during resting-state, participants were asked to answer visual
analogue scales (0–100) inquiring as to whether they were internally
singing or employing meditation in the scanner.Given the time delay,
questions pertaining to their recollection of each resting state acquisition
were also provided. For more information regarding all inventories, see
the Supplementary.
2.3. Image acquisition
Images were acquired on a MAGNETOM 7T MRI scanner. On each
visit, participants underwent a structural MRI (60 min post-treatment),
single-voxel proton MRS in the PCC (70 min post) and visual cortex
(80 min post), and fMRI (90 min post), during peak subjective effects.
Findings and methods pertaining to MRS are to be reported elsewhere.
T1-weighted anatomical images were acquired using a magnetisa-
tion-prepared 2 rapid acquisition gradient-echo (MP2RAGE) sequence
(TR = 4500 ms, TE = 2.39 ms, TI1 = 0.90 s, TI2 = 2.75 s, flip angle
1 = 5°, flip angle 2 = 3°, voxel size = 0.9 mm isotropic, matrix
size = 256 × 256 × 192, phase partial Fourier = 6/8, GRAPPA = 3
with 24 reference lines, bandwidth = 250 Hz/pixel). 500 echo planar
imaging (EPI) volumes were acquired at rest (TR = 1400 ms;
TE = 21 ms; field of view=198 mm; flip angle = 60°; oblique acquisi-
tion orientation; interleaved slice acquisition; 72 slices; slice thick-
ness = 1.5 mm; voxel size = 1.5 × 1.5 × 1.5 mm) followed by 5
phase encoding volumes acquired for EPI unwarping. Shimming was
performed with fixed criterion for all scans, with dorsal regions priori-
tised and the cerebellum excluded. Partial coverage of the temporal
pole (< 100 voxels) arose for 4/42 scans. During EPI acquisition, par-
ticipants were shown a black fixation cross on a white background.
2.4. Functional pre-processing
All pre-processing steps were performed according to an in-house
pipeline (Amico et al., 2020;Amico et al., 2017) based on FSL (FMRIB
software library, FSL 6.0; www.fmrib.ox.ac.uk/fsl) and implemented in
MATLAB (R2019b). The individual functional connectomes (FCs) were
modelled in the native BOLD fMRI space of each subject.
MP2RAGE images were first denoised to improve signal-to-noise ra-
tio (Choi et al., 2019), bias-field corrected (FSL FAST), skull-stripped
(HD-BET) (Isensee et al., 2019), and then segmented (FSL FAST) to ex-
tract white matter, grey matter and cerebrospinal fluid (CSF) tissue
masks. These masks were warped in each individual subject's functional
space by means of subsequent linear and non-linear registrations (FSL
flirt 6dof, FSL flirt 12dof and FSL fnirt). BOLD fMRI volumes were pre-
processed in line with Power at al. (Power et al., 2014;Power et al.,
2012). Subsequent steps included: deletion of 2 initial volumes (FSL
utils), slice timing correction (FSL slicetimer), BOLD volume unwarping
(FSL topup), realignment (FSL mcflirt), normalization to mode 1000,
demeaning and linear detrending (Matlab detrend), regression (Matlab
regress) of 18 signals: 3 translations, 3 rotations, and 3 tissue-based re-
gressors (mean signal of wholebrain [global signal], white matter [WM]
and cerebrospinal fluid [CSF]), as well as 9 corresponding derivatives
(backwards difference; Matlab). A bandpass first-order Butterworth fil-
ter [0.009 Hz, 0.08 Hz] was then applied to all BOLD timeseries at the
voxel level (Matlab butter and filtfilt). As an additional cleaning step,
the first three principal components of the BOLD signal in the WM and
CSF tissue were subsequently regressed out of the grey matter (GM) sig-
nal (Matlab, pca and regress) at the voxel level, in line with the aComp-
Cor methodology described by Behzadi et al. (Behzadi et al., 2007).
These principal components were included as nuisance parameters
within a general linear model for the BOLD time series data extraction.
No smoothing was performed.
We also kept track of the fMRI volumes that were highly influenced
by head motion, by using three different metrics as a scrubbing index:
1) Frame Displacement (FD, in mm); 2) DVARS (D referring to temporal
derivative of BOLD time courses, VARS referring to root mean square
variance over voxels) (Power et al., 2014);3) SD (standard deviation of
the BOLD signal within brain voxels at every time-point. Outlier BOLD
volumes were defined as having a 1) FD > 0.5; 2) DVARS > 75 per-
centile + 1.5 of the interquartile range; 3) SD > 75 percentile + 1.5 of
the interquartile range. It should be noted no volume censoring was
performed using this index. Rather, this information was used as a con-
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P. Mallaroni et al. NeuroImage xxx (xxxx) 120480
found in our multilinear regression analyses and quality control assess-
ments (see Fig. 6 and Figure. S2). Functional connectomes obtained
with and without scrubbing were highly similar (average Pearson's
r = 0.99) with no significant differences in motion being identified be-
tween or within conditions (see Figure. S2).
A 2 mm cortical Schaefer parcellation (Schaefer et al., 2017) based
on 200 brain regions (publicly available at:https://github.com/
ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/
Schaefer2018, was projected into each subject's T1 space (FSL flirt 6dof
and FSL flirt 12dof) and subsequently their native EPI space. FSL
boundary-based-registration was also applied to improve the registra-
tion of the structural masks and parcellation to the functional volumes.
Regions of interest (ROIs) were ordered according to seven cortical
RSNs as proposed by Yeo et al. (Yeo et al., 2011). These included the vi-
sual (VIS), somatomotor (SM), dorsal attention (DA), ventral attention
(VA), limbic (L), frontoparietal (FP) and the default mode network
(DMN).
2.5. Assessment of functional connectivity
To assess connectome fingerprints (described in our next section)
across static and dynamic temporal scales, we devised two separate
workflows for functional connectivity. Static functional connectivity
between each pair of ROIs (edges) was calculated with a Pearson corre-
lation coefficient between each pair of mean signal time courses (across
a single run). For each subject, this results in an N×NFC matrix,
where N is the number of ROIs, and with each element in the FC repre-
senting the connectivity strength between a pair of ROIs. Secondly, we
assessed the dynamic functional connectomes (dFC) by performing a
sliding window analysis to produce sets of connectivity matrices reflect-
ing the temporal development of whole-brain functional connectivity
(across our 249 timepoints). We captured relevant FC patterns by bal-
ancing the number of time points for a stable dFC computation, explor-
ing sets of dFCs across 5 different window lengths of: 70 s, 140 s, 210 s,
280 s and 349 s. Each window step was fixed to 14 s, the equivalent of
10 TRs as per prior work (Van De Ville et al., 2021).
2.6. Whole-brain connectome identifiability
Changes to the identifiability of each subject's functional connec-
tome were quantified by replicating the methodology originally pro-
posed by Amico et al., devised for both static and dynamic functional
connectivity (Amico and Goñi, 2018;Van De Ville et al., 2021).
The approach devises an identifiability matrix for each condition,
consisting of a matrix of correlations (Pearson, square, non-symmetric)
between a subject's test and retest functional scans. We firstly split each
scan into two corresponding halves (249 vol each, or 6 min) to generate
test-retest sets for each condition. Prior work has shown fMRI scan
lengths of 3 min are sufficient to produce reliable fingerprints (Amico
and Goñi, 2018;Airan et al., 2016). Since connectivity matrices are
symmetric, we can then extract unique elements of each test-retest FC
by taking the upper triangle of each matrix; resulting in a 1 × 19,900
vector of edge values for each subject per condition which can then be
compared using Pearson correlation, either between different subjects
in the same condition or within the same subject across conditions. This
yields the “identifiability matrix”as outlined in Fig. 2.
2.6.1. Static identifiability
In the case of static FCs, let Abe the “identifiability matrix”, be-
tween the subjects’FCs test and retest. The dimension of Ais N2, where
N is the number of subjects in the study. In it's original formultation
(Amico and Goñi, 2018), let Iself = <aii> represent the average of the
main diagonal elements of A, which consist of the Pearson correlation
values between test-retest sets of same subjects, otherwise defined as
self-identifiability or Iself. Similarly, let Iothers = <aij> define the aver-
age of the off-diagonal elements of matrix A, i.e., the correlation be-
tween test-retest sets of different subjects. Lastly, let the differential
identifiability (Idiff) of the population be the difference between both
terms, otherwise denoted as:
Which provides an indication of the difference between the average
within-subject FCs similarity and the average between-subjects FCs
similarity. The greater the Idiff the higher the individual fingerprinting
value (or “uniqueness”) across the sample. In the present study, by ex-
trapolating each individual element of A(see Fig. 2) we can define Idiff,
Iself and Iothers scores per subject. As an additional step, we also sought to
derive the distance of each participant fingerprint under ayahuasca
(i.e., Iself Iothers Idiff) from their respective normative state from their
baseline. Using the approach outlined by Sorrentino et al. for static con-
nectomes (Sorrentino et al., 2021) we calculated the identifiability ma-
trices across combinations of different conditions (e.g., the Pearson cor-
relation of test-sober, retest-ayahuasca). When concatenated with our
within-group identifiability matrices this produces a hybrid identifia-
bility matrix (see Figs. 2 and 3), where the between blocks (groups) ele-
ments and scores reflect the similarity (or distance) between the test-
retest connectomes of subjects across different conditions. By averag-
ing, this also allows us to derive a final overall cohort Iclinical score
which provides a percentage (average) score of how similar their con-
nectome with respect to baseline is (0% - totally dissimilar, 100% to-
tally equivalent) across test-retest splits. Finally, we also measured the
success-rate (SR) of the identification procedure as percentage of cases
with higher Iself vs Iothers (Finn et al., 2015;E. Sareen et al., 2021). For
completeness, we calculated per condition the significance of both ob-
served Idiff and SR scores in respect to their null equivalents using per-
mutation testing (see Supplementary).
2.6.2. Dynamic identifiability
We can next extend this principle to dynamic functional connec-
tomes (dFC) by calculating each measure across each dynamic frame of
connectivity (see Fig. 2 for an overview). For a fixed window length w,
the resulting dynamic identifiability matrix is then a block diagonal ma-
trix, where each block represents the self-similarity within the dFC
frames of a specific subject. The off-diagonal blocks, in this representa-
tion, encode instead the between-dFC frames similarity across different
subjects (dynamic Iothers). Let = {dFC1, dFC2,…, dFCN} be the set
of dFC frames in the test session for a specific subject M. Similarly, let
represent the set of dFC frames in the retest session for the same
subject M. We can then define the dynamic Iself (dIself)for subject Mas:
Where by , define the cardinalities of the sets. Simi-
larly, let , define the sets for a different subject F. We can
define dynamic Iothers (dIothers)as:
and hence:
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P. Mallaroni et al. NeuroImage xxx (xxxx) 120480
Fig. 2. Connectome fingerprinting workflow. First, each subject fMRI timeseries is split into test vs retest halves. For all subjects and conditions (baseline,
ayahuasca), dFC frames are computed for increasing temporal windows until tmax is reached. Connectome fingerprints can next be calculated as the similarity in
functional connectivity for all combinations of FC test vs retest (within condition and between), yielding an identifiability matrix (Wu et al., 2022) per timescale
(left). Each colour matched block reflects identifiability within a condition whereas colour mismatched (“hybrid”) blocks represent the distance of each subject's
identifiability between conditions. This object allows us to compute for each subject: Iself (represented by each diagonal element) denoting their similarity to oneself
and Iothers (represented by the average of each row/column) representing their similarity to others, for both within and between conditions. In parallel, we can assess
the fingerprinting value of specific edges per condition and timescale by calculating their intraclass correlation coefficient (ICC, right). We can next rank edges ac-
cording to their ICC and iteratively calculate a compound measure of Iself and Iothers (Idiff). This allows us to examine how edges "driving" one's fingerprint evolve
under Ayahuasca. Lastly, we can assess their experiential relevance by fitting an iterative multi-linear model comprising PCA-derived principal components (PCs)
of their functional connectivity as predictors of interest. Decomposing their signal into components ranked according to explained variance (PC1–3), the relevance
of cohort-level (high-variance –PC1) and individual-level functional connectivity information (low-variance –PC3) is simultaneously assessed, while accounting
for motion and “working”. At each step, model performance and generalisability are measured using k-fold (K5) cross validation, yielding an optimal edge cut-off.
where the summation is over the total number of subjects Sother
than M. Last, dIdiff for a subject Mcan be described as:
2.7. Edgewise connectome identifiability
In order to understand which edges key contributors were to
changes in connectome identifiability, we quantified the edgewise reli-
ability for each brain region pair and every test-retest scan per subject
using intraclass correlation analysis as established Amico et al. (Amico
and Goñi, 2018). Coefficients derived from ICC are widely used as a re-
liability index in test-retest analyses, reflecting the percentage agree-
ment between two units of measurement (e.g. an edge) within the same
group (e.g. a subject) (McGraw and Wong, 1996;Noble et al., 2019).
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P. Mallaroni et al. NeuroImage xxx (xxxx) 120480
Fig. 3. Whole-brain measures of static identifiability. (A) shows the identifiability matrix (far left) at Tmax with corresponding “standard”identification matri-
ces for each condition expanded on the right. From hybrid off-block elements one can also define the Iclinical for a participant as the average similarity of the indi-
vidual connectome of a subject with respect to the baseline. For all, differential identifiability (Idiff) values and success rates (SRs, where applicable) on top also
provide complementary scores of the fingerprint level (see Methods). (B) Violin plots highlighting the difference of each identifiability metric (Idiff, Iself, Iothers) be-
tween conditions. Hybrid counterparts are also presented in respect to baseline. Two-tail significance is denoted as follows: p< 0.05*, p< 0.01**, p< 0.001***.
The greater the ICC value, the greater the consistency these two units
hold. For reference, ICC values below 0.40 are suggested to be poor/un-
reliable whereas those beyond 0.90: excellent/congruent (Cicchetti and
Sparrow, 1981).
We employed this approach under the assumption that subsets of
highly stable edges (edges holding high ICC scores) across test-retest
sets edges are major drivers of each state's connectome fingerprint. For
a FC, this generates a square symmetrical ICC matrix of size N2, where
N is the number of brain regions (see Figs. 4 and 5) for a specific time-
frame. From this, nodal ICC strength (i.e. regional ICC scores) can be
characterised by summing ICC values across rows. We also extrapolated
network identifiability by averaging ICC values of within and between
network edges, producing 7 × 7 ICC fingerprint matrices correspond-
ing to our Yeo parcellation. Note that edges were thresholded according
to lower bounds of ICC (0.40).
In the case of dynamics, there might be FC frames where identifica-
tion is higher than others. Consequently, this might not reflect the aver-
age behaviour depicted by dIdiff thereby skewing ICC estimates. To
cover this necessity, for each subject session, we sorted the dFC frames
in test-retest according to their similarity, from highest to lowest, based
on their dIself value. We then recalculated dIself, dIothers, and dIdiff when
iteratively adding dFC frames one at the time, starting from the best
matching ones and then proceeding based on their similarity values in
order to end with a “top”frame for each timescale on which our ICC
analyses could be performed. As a supplementary analysis we also ex-
amined the relationship between FC variability and stability by calcu-
lating the standard deviation of functional connectivity for each frame
(see figure S4.).
With the expectation that subsets of static edges might primarily
contribute to each condition's identifiability, we sorted these according
to their thresholded ICC values computed on the baseline condition
(baseline). Edges were added in a descending fashion, with Idiff being
recalculated at each iteration of 50 edges. By defining Idiff as a target
variable, the optimization problem during edge ranking of differential
identifiability (total uniqueness) is then simplified to maximizing Idiff.
We selected the sober condition as an index to visualise the evolution of
normative drivers maximally contributing to Idiff (in other words, edges
driving a subject's total fingerprint score).
2.8. Edgewise prediction of subjective experience
In light of the individual nature of subjective experience and con-
nectome fingerprints we opted for an iterative multilinear regression
modelling approach (MLR) similar to connectome predictive modelling
(Shen et al., 2017). Aiming to assess the relative explanatory power of
connectome fingerprints for inter-individual differences in subjective
experience, we employed principal component analysis (PCA) decom-
position (Jolliffe and Cadima, 2016) of the functional connectivity val-
ues of highly identifiable edges (subjects x edges). An unsupervised ex-
ploratory approach, PCA is a dimensionality reduction approach used
to transform a high-dimensional dataset into a lower-dimensional space
while retaining most of the sample's variance. Notably, the resultant
principal components (PCs) are a set of uncorrelated vectors that repre-
sent the directions of maximum variance in the data. Consequently, this
allows us to isolate a subset of maximally heterogeneous and indepen-
dent FC motifs based on idiosyncrasy which may best reflect variable
behavioural traits. This decomposition was applied in an iterative fash-
ion for model selection. Firstly, all static edges driving idiosyncrasy in
the ayahuasca condition were sorted in descending order according to
their ICC value. At each iteration of 50 edges, we performed a PCA de-
composition of these demeaned n, retaining three PCA components
ranked according to explained variance. Then, a MLR was built for each
subjective effect measure, comprising these three PCA components as
predictors of interest alongside two covariates: singing (self-reported
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Fig. 4. Whole-brain measures of dynamic identifiability. (A) Dynamic identifiability matrices at five different window lengths (70, 140, 210, 280 and 349 s) for
each condition. The dynamic differential identifiability (Idiff) values and success rates (SRs) on top of each matrix provide two complementary scores of the finger-
print level of the dataset across temporal scales (see Methods). (B) Violin plots highlighting differences in each identifiability metric (Idiff, Iself, Iothers) per timescale.
Subjects are represented with single points. Two-tail significance is denoted as follows: p< 0.05*, p< 0.01**, p< 0.001***.
internal singing during the resting-state) and scrubbing (number of
valid volumes). Absence of multicollinearity was assessed using vari-
ance inflation factor (VIF) (Craney and Surles, 2002). At each iteration,
we strengthened the reliability of our model using k-fold cross-
validation (Fushiki, 2011) with k= 5. Specifically, kiterations were
performed and at each iteration the kth subgroup was used as a test set.
For each iteration, the Spearman's correlation coefficient between pre-
dicted and actual inventory values was calculated and considered as a
performance score. We assessed the reliability of this performance score
against surrogate models, computed using a set of randomly permuted
edges at each step. For each variable of interest this process was re-
peated 100 times. This iterative permutation approach provided a cut-
off point for edges maximally contributing to predictive performance
by identifying a set number of edges at which model performance out-
performed its surrogate model's performance standard deviation while
balancing explained variance (see Figure S6.). It is worth mentioning
that while it is common practice to define the number of retained PCs
(1…p) such that their cumulative explained variance is close to 100%,
our decision of retaining 3 PCs was defined by a I) Tolle et al. demon-
strating p = 3 provides an optimum trade-off between explained vari-
ance and overfitting in idiosyncracy-informed modelling (Tolle et al.,
2023) and II) using an unrestricted approach would have required us to
change pfor each iteration and null models, limiting model compar-
isons.
2.9. Statistics
Statistical analyses were carried out in MATLAB 2019b. Shapiro-
Wilks was firstly used to assess the normality of all measures. Control
variables (subjective effects, PK) were assessed by means of one-tailed t-
tests against zero. All other outcome measures were analysed in a two-
tailed fashion according to their normality; either by Wilcoxon sign-
rank (W) or paired-sample t-tests (t) with Cohen's d effect sizes (d) be-
ing provided for each. Observed static identifiability scores (true) val-
ues were examined against corresponding null-distributions following a
permutation testing framework (see supplementary). Regarding net-
work-based statistics, we retrieved a conservative Bonferroni-holm cor-
rected p-value (pbonf) according to the number of unique elements in
each matrix. The alpha criterion of significance for all inferences was
set at p<0.05.
3. Results
Experienced members of Santo Daime were enroled in a fixed-order,
within-subject, observational study. A baseline (sober) resting-state
fMRI was followed 1 day later with a second acute (ayahuasca) fMRI
scan 90 min after communal intake (i.e., peak effects). The study also
entailed pharmacokinetic sampling, questionnaires pertaining to retro-
spective drug effects and aspects of “work”during resting-state (see
Methods). Of the 24 patients recruited, 3 were excluded due to exces-
sive fMRI head motion. Demographic information pertaining to the
imaging sample can be found in Table S1.
3.1. Acute effects of ayahuasca
Ayahuasca intake was associated with increased ratings on all (sub)
dimensions of the 5D-ASC (max. oceanic boundlessness: t= 7.29, p
< 0.0001, d= 1.59; min.changed meaning of percepts: t= 3.90, p
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Fig. 5. Spatial specificity of connectome fingerprints. (A) Edgewise intraclass correlation (ICC) matrices per condition (baseline, ayahuasca). The ICC matrices
are shown thresholded at 0.4. All 7 functional networks as defined by Yeo et al. (see Methods) are highlighted by black boxes: VIS = visual network; SM = somato-
motor network; DA = dorsal attentional network; VA = ventral attentional network; L= limbic network; FPN = fronto-parietal network; DMN = default-mode
network. (B) Differences in network ICC values between conditions. For each condition, ICC edgewise scores are grouped per Yeo functional network and compared
using Bonferonni-corrected two-tail sign-rank testing. Approximated z-scores are then extrapolated and plotted for ease of visualisation. (C) Identification of top fin-
gerprinting edges. Idiff scores were obtained by iteratively calculating identifiability matrices for each condition, ranked according to those contributing the most to
baseline identifiability (as per ICC values). Lines represent condition means, with shading reflects the standard deviation of Idiff across subjects at each step. (D).
Nodal strength (sum across unthresholded ICC regional matrix rows) across subsets of top fingerprinting edges per condition. For each render percentiles are shown
(from 20th to 80th percentile). For all plots, two-tail significance is denoted as follows: p< 0.05*, p< 0.01**, p< 0.001***.
< 0.0004, d = 0.85), and for the EDI (t = 7.15, p<0.0001,
d= 1.56). Mean ratings of oceanic boundlessness (OBE. 33.1% [SEM:
4.54]) and visual restructuralization (VR. 26.24% [SEM: 4.37]) were
most affected. Tandem pharmacokinetic analyses also demonstrated
serum concentrations of DMT (the principal psychoactive constituent of
ayahuasca) were significantly greater than zero at both 60 (t= 4.82, p
< 0.0001, d= 1.14) and 160 min (t= 5.16, p< 0.0001, d= 1.18)
after intake.
During resting-state acquisition, participants reported significantly
more internal singing under ayahuasca (W= 58, p= 0.0261,
d= 0.63). Levels of engagement in meditation did not significantly dif-
fer between conditions. A full characterisation of all inventories and
serum alkaloids can be found in the supplementary materials.
3.2. Quantifying whole-brain fingerprints
Connectome fingerprinting provides a window into the “unique-
ness”of one's functional connectivity (Amico and Goñi, 2018;Van De
Ville et al., 2021;Sorrentino et al., 2021). This approach stems from the
simple assumption that a FC should hold greater similarity between
test-retest scans of the same subject than between different subjects
(Finn et al., 2015). By computing an “Identifiability matrix”we can ex-
trapolate for a subject whole-brain metrics reflecting both the intra-
individual (Iself) and inter-individual (Iothers) variability of their func-
tional connectome (see Methods, Fig. 2). These measures can be com-
pounded as an overall fingerprinting score (Idiff) in other words, how
well a subject can be identified within a group of other subjects based
on their connectome. As a first pass, we explored changes to whole-
brain fingerprints and their dynamical counterparts. We derived mea-
sures of identifiability for static FCs (Fig. 3) and their dynamic equiva-
lents (Fig. 4) by replicating our analyses across increasing window
lengths (see methods). In each case we also provide success rate (SR)
(Finn et al., 2015;E. Sareen et al., 2021) as a supplementary assessment
Static identifiability. As depicted in Fig. 3B, sign-rank testing re-
vealed the differential identifiability (Idiff) of each participant was
significantly diminished under ayahuasca (W= 53, p = 0.0298,
d= 0.35)reflecting an overall reduction in a subject's FC idiosyn-
crasy. If we examine its constituents, this effect was driven by a sig-
nificantly increased Iothers score (t= 2.72, p = 0.0131, d = 0.59).
In other words, participant connectomes significantly mirrored one
another's under ayahuasca, depicted by the saturation of off-diagonal
elements under ayahuasca (Fig. 3A). Remarkably, subjects continued
to retain high Iself (p> 0.05)and SR scores, reflecting a preserved
idiosyncrasy under ayahuasca. It should be noted that each condi-
tion's Idiff and SR scores were also significantly greater than their null
equivalents following permutation testing (p= 0.001, see Fig-
ure.S1).
We then examined how dissimilar might the constituent edges of a
subject's fingerprint scores be under ayahuasca. Hybrid equivalents of
our identifiability matrices (see Fig. 2, Methods) enabled us to derive
the “distance”of each subject's score (ie their relative composition)
from baseline. Doing so, we identified a greater dissimilarity between a
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subject's functional connectome under ayahuasca versus baseline, with
both IselfHybrid (t=−8.67, p < 0.0001, d = 1.89 and IdiffHybrid (t=
−7.94, p <0.0001, d= 1.74) significantly reduced. In other words,
while a subject's scalar self-identifiability score remains the same under
ayahuasca, its constituent edges differ relative to baseline. This can be
visualised as the faded diagonals in the off block “Hybrid”elements of
Fig. 3A and further reflected by a low Iclinical score (39.21%).
Dynamic identifiability. Might specific timescales of neural pro-
cessing further account for these global differences? Repeating this
previous analysis across increasing window size, reveals an equivalent
pattern. As per prior work (Van De Ville et al., 2021), dynamic (Idiff)
increased steadily with longer window lengths (Fig. 4A) as a by-
product of the increasing number of timepoints for dFC computation
with early dynamical fingerprints (designated by clear diagonal ele-
ments) arising at shorter temporal intervals.
Replicating our analyses across each temporal scale, we observed
the equivalent patterns of change. As shown in Fig. 4B, Idiff was signifi-
cantly reduced under ayahuasca in a temporally selective manner
(across 140s-349 s; max.220s: W= 51, p= 0.0250, d= 0.41). This
effect was partly accounted by Iothers increasing across select frames
(210–349 s max.349s: t(20) = 2.72, p = 0.0131, d= 0.59).Once
more, Iself was found to remain stable at all timescales (p> 0.05). For
all windows, IselfHybrid was significantly reduced under ayahuasca
(max.210s: t= 9.39 p < 0.0001, d = 1.74), as well as IdiffHybrid
(max.140s: t= 9.81 p < 0.0001, d= 2.16).
3.3. Select edges mediate reductions in connectome identifiability
Identifying global changes to each subject's connectome fingerprint
under ayahuasca, we then sought to understand their spatiotemporal
profiles. To do so, we applied an edgewise ICC to investigate the finger-
printing value of edges (see Methods, Fig. 2) pertaining to canonical
resting-state networks (RSNs).
Static connectomes. We observed global reductions in ICC scores
(W= 92,864,811, p <0.0001, d = 0.05) under ayahuasca.While this
suggests a connectome-wide drop of temporal stability, individual
RSNs have varying levels of importance for fingerprints and may be
differentially affected (Fig. 5A). Focusing on network properties (Fig.
5B), within-network analyses following Bonferrroni correction re-
vealed significant reductions in stability for the ventral attentional
(VA. W= 8414, pbonf = 0.0014, d= 0.27)and converse increases for
the dorsal attentional network (DA. W= 30,021, pbonf = 0.0283,
d= 0.20)under ayahuasca. In contrast, inspection of between-
network pairs reveals reductions in stability were primarily attribut-
able to the visual (VIS) and VA functional subsystems (W= 36,803,
pbonf <0.0001, d = 0.49). Within these, certain network pairs such as
SM-L (W= 25,535, pbonf <0.0001, d = 0.26) and VIS-DA connectiv-
ity (W= 122,508, pbonf = 0.0008, d = 0.14) exhibited greater stabil-
ity under ayahuasca.
Given that subsets of highly synchronous edges are important con-
tributors to normative connectome fingerprints (Amico and Goñi, 2018;
E. Sareen et al., 2021;Van De Ville et al., 2021;Sorrentino et al., 2021),
we investigated how they might shift in importance under ayahuasca.
Ranking baseline edges from most to least stable, we recalculated each
subject's identifiability 50 edges at a time. Fig. 5C shows that, while
baseline, or “normative”identifiability can be maximised within 250
edges, the contribution of these edges to fingerprinting drops markedly
under ayahuasca (Idiff. t(249) = −10.12, p<0.0001, d= 2.38).
Therefore, edges otherwise normally “driving”a subject's identifiability
are no longer significant contributors when under the influence. Rather,
a reconstitution of edge importance becomes apparent when examining
their nodal equivalents (Fig. 5D). One can notice connections impli-
cated in hubs pertinent to DA, VA and SM networks are instead primar-
ily replaced by those pertinent to the DMN.
Dynamic connectomes. We then examined differences in spatial
ICC patterns as a function of time by repeating our analysis across
each timescale. As window length increases, one can note different
networks appearing at different rates, such as sensory networks at
shorter intervals or the DMN at slower scales (Fig. 6A). This gradient
highlights the varying temporal prerequisites of RSN fingerprints (Van
De Ville et al., 2021). Our ICC analyses revealed global reductions in
dFC stability across all measured timescales (max.70s:
W= 79,109,036, p <0.0001 d= 0.18)under ayahuasca.
As shown in Fig. 6B, network-based analyses revealed diffuse
changes to the stability of dynamic functional connectivity under
ayahuasca (for a full characterisation see tables S5.1–5). Novel reduc-
tions in within-network edge stability were identified across increasing
windows of time for: the DMN (max.210s: W= 171,684, pbonf
<0.0001, d= 0.18); VA (max.280s: W= 6549, pbonf <0.0001
d = 0.37) and DA networks (max.70s: W= 8250, pbonf = 0.0003,
d= 0.245). Contrarily, VIS network edges exhibited greater stability at
280 s (W= 48,931, pbonf = 0.0122, d= 0.13). In parallel, reductions
in between-network edge stability populated all scales. This attenuation
could be primarily ascribed to edges involved in between-network SM
and VIS connectivity (max.VIS-SM (70 s): W= 82,429, pbonf <0.0001,
d= 0.47).Furthermore, previously identified static increases in be-
tween-network SM-L connectivity stability was found to be time-
dependant (280 s. W= 18,936, pbonf <0.0001 d= 0.16).We also ex-
amined whether changes to standard deviation of different RSNs (see
supplementary materials) might also help explain changes to the topog-
raphy of edge stability. In this regard, while between-network reduc-
tions in functional connectivity variability was observed, no clear asso-
ciation could be ascertained (see Figure.S4).
We next asked whether altered fingerprint dynamics under
ayahuasca could also be reflected at a regional level of brain organisa-
tion. Identifying each region's ICC maximum, we summarised their tem-
poral optimums as a brain render (Fig. 6C). Typically, transmodal re-
gions comprising association cortices, “peak”at longer temporal win-
dows whereas unimodal regions, such as primary sensory areas, arise
early on (Van De Ville et al., 2021). While this was the case at baseline,
this temporal gradient shows an inversion effect following intake, best
demonstrated by regions such as the prefrontal cortex peaking early on
or vice versa for unimodal areas such as the visual cortex.
3.4. Connectome fingerprints are predictive of perceptual drug effects
We lastly performed an exploratory analysis investigating the be-
havioural relevance of connectome fingerprints. We hypothesised that
highly idiosyncratic edges under ayahuasca could also predict meaning-
ful aspects of a subject's subjective experience. To assess the behav-
ioural relevance of idiosyncratic edge connectivity, we built an iterative
multilinear model approach comprising PCA components of subsets of
ICC-ranked edges as predictors of interest for our subjective effect mea-
sures. A k-fold cross validation revealed that peak predictive perfor-
mance for the 5D-ASC dimensions Visual Restructuralisation (VR) and
Auditory Alterations (AA) was achieved using the top 3000 most stable
edges (see Figure S6.), with predictive performance for all other out-
come measures being no better than the null model.
Other than three edge-based PCA components, each model also
comprised two other predictors: scrubbing and internal singing. We
found that of all PCA components, only PCA3 significantly increased
the predictive power of the model forVR (F(4,20) = 1.98; R2= 0.45;
p= 0.0396; β=−5.59) and AA (F(4,20) = 4.8; R2= 0.70;
p= 0.0016; β=−2.17. Together, this finding might reflect the fact
that subsets of edges are predictive of each dimension only when con-
sidering the idiosyncracy of functional connectivity. Edges most impli-
cated were primarily found in FPN, DMN and DA hubs as well as the re-
gions pertaining to the VIS (Fig. 7C). As a precaution we also examined
whether the motion (nScrub) or shared behaviour (Singing) were rele-
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Fig. 6. Temporal specificity of connectome fingerprints. (A) Mean edgewise intraclass correlation (ICC) matrices per condition (baseline, ayahuasca) at each
timescale. The ICC matrices are shown thresholded at 0.4, the cut-off for a reliable ICC score (Finn, 2021). All 7 functional networks as defined by Yeo et al. (see
Methods) are highlighted by the black boxes: VIS = visual network; SM = somatomotor network; DA = dorsal-attention network; VA = ventral-attention network;
L= limbic network; FPN = fronto-parietal network; DMN = default-mode network. (B) Differences in network ICC values across timescales. For each condition
and per window, ICC edgewise scores are averaged across Yeo functional networks and compared using Bonferroni-corrected two-tail sign-rank testing. Approxi-
mated z-scores are then extrapolated and plotted for ease of visualisation. Lighter hues reflect increases in ICC values under ayahuasca whereas darker ones reflect di-
minishments under ayahuasca. (C) Mean temporal peaks of nodal stability. Maximum values across temporal profiles at each brain node are overlaid onto a brain
render to map the time scales of human brain fingerprints. The maximum value for each brain node was derived from ICC nodal strength values (sum across ICC re-
gional matrix rows) at each window per condition. For all plots, two-tail significance is denoted as follows: p< 0.05*, p< 0.01**, p< 0.001***.
vant predictors, finding no significant contribution to model perfor-
mance.
3.5. Additional control analyses
For completeness, we performed a series of quality control analyses
on our primary identifiability findings. Specifically, we (i) repeated our
main analyses using a coarser Schaefer 100-node parcellation, (ii) using
censored fMRI timeseries, (ii) assessed differences in motion metrics be-
tween conditions, (iii) examined split-half differences in primary mo-
tion outcomes per scan, (iv) evaluated their association with all sFC and
dFC identifiability outcomes. Our findings appear robust to motion,
parcellation resolution and replicable across different denoising strate-
gies (Figure.S2-S5.).
4. Discussion
Here, we leveraged the understanding that an individual's brain
functional connectivity profile is both unique and reliable to document
how the inherent features of a subject's functional connectome might
transition into a collective altered state of consciousness. Using the con-
cept of connectome fingerprinting outlined by Amico et al. (Amico and
Goñi, 2018), in a cohort of 21 Santo Daime members taking part in the
ritualistic use of ayahuasca, we were able to detect for each subject a
significantly greater proportion of shared functional connectivity traits
across different timescales of neural processing. Furthermore, we show
that this shared variance is accompanied by the reconfiguration of key-
point edges pertinent to higher-order functional subsystems, otherwise
driving normative brain “fingerprints”. Equally, we show that the insta-
bility of edges is likely relevant to experiential differences given that
they can be used to predict aspects of an individual's subjective experi-
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Fig. 7. Peak predictive multi-linear model of subjective effects. Following an ICC derived feature selection comprising k-fold validation and null-modelling (see
Methods), 3000 edges were found to yield explanatory power. (A) Visual restructuralisation (VR) peak performance model. Left: The additive linear model consists
of two nuisance variables (nScrub, Singing), and three PCA predictors (PC1–3); Right: Scatter plot of the observed VR scores versus predicted model VR scores. (B)
Auditory alteration (AA) peak performance model. As before, left: incorporated predictors in an additive linear model, Right: observed vs predicted AA scores. (C)
PC3 coefficient loadings. Nodal strength (sum across regional matrix rows) and corresponding network means of the significant PC3 coefficient loadings for the top
3000 edges are depicted. For all panels, significant predictors are denoted as follows: p<0.05*, p<0.01** with β- indicating that its beta coefficient is negative.
ence. All in all, these findings point to the general blueprint of a subjec-
t's inherent resting-state functional connectivity shifting to overlap with
fellow members (see Fig. 8 for an analogy). Ultimately, subject-level ap-
proaches such as those presented herein highlight the potential to dis-
cover personalised fMRI-based connectivity markers that may eventu-
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Fig. 8. Graphical analogy of connectome fingerprint shifts under ayahuasca. In everyday life people rarely dress one and the same. Ordinarily, an individual
might choose their attire based on personal preference, such as a colourful shirt. The colour palette that we might choose would represent our distinctiveness, in
turn differentiating us from others (Iothers). Throughout our day, Iothers might vary, given others with distinct preferences might come and go. Now say in a differ-
ent scenario, such as a Santo Daime ceremony, we were to abide by the dress code of a white uniform. Even if others might come and go, our similarity to others
(Iothers) at the ceremony would be high since the colour white is mandated throughout the event for all participants. However, if the uniform were to be contrasted
to daily life (IothersHybrid) we might find it to be just as dissimilar as any other coloured shirt that we might come across on an average day. In parallel the con-
stituents of our self-identity (Iself) may equally be denoted as a unique pattern; Whereas the total level of distinctiveness is unlikely to change regardless of circum-
stance, an acute perturbation by a pharmacological agent such as ayahuasca may change the pattern's constitution (IselfHybrid).
ally be used to trace a subject's functional connectome across states of
consciousness.
4.1. The collective use of ayahuasca is associated with shared connectome
fingerprints
Whereas we found the Idiff of a subject's connectome was diminished
under ayahuasca, this reduction could be ascribed to the increased con-
tribution of Iothers. In other words, individual connectome fingerprints
were found to be less idiosyncratic under the influence of ayahuasca.
When factoring in the practices of Santo Daime, this could echo the
shared practices carried out by church members. Recent replication of
the present methodology in a sample of recreational users not holding
shared rituals yielded compatible inverse findings of enhanced FC idio-
syncrasy (reductions in Iothers and elevations in Idiff) under the 5-HT2A
agonist psilocybin (Tolle et al., 2023). Evidence from classical psyche-
delics suggests an “unconstrained”state of cognition of few deliberate
or automatic constraints, featuring a large amount of hyper-associative
thinking and diminished reality-testing (Girn et al., 2020). While
ayahuasca experiences are highly subjective, followers of Santo Daime
share stereotyped behaviours otherwise absent in recreational users,
such as singing or attentional deployment, synonymous with a con-
strained state of cognition. For example, prior imaging work with
church-goers has previously drawn parallels with the induction of a
task-active state, exemplified by suppressions in DMN activity
(Palhano-Fontes et al., 2015) normally associated with external goal-
directed attention such as task engagement or focused attention medita-
tion (Scheibner et al., 2017;Tripathi and Garg, 2022). Furthermore,
studies with normative samples demonstrate that the inter-individual
variability of a sample is diminished when engaging in a task battery,
proportionally to cognitive load (Geerligs et al., 2015;Finn et al.,
2017). Thus, an interpretation could be that such a constrained mental
state is disseminated across individual connectivity matrices.
While the appearance of shared functional connectome fingerprints
pertained to dFC timeframes at which complex cognition emerges (Van
De Ville et al., 2021), it however, cannot be definitively stated whether
this shared variance is solely attributable to group behaviour.
Ayahuasca itself was not administered in any other context, such as for
example, the individuals alone. It may very well be the case that
ayahuasca alone may propagate across connectivity matrices in the
manner described, given DMT's particularly potent effects on functional
brain organisation (Timmermann et al., 2023). Without future work
disentangling the many spontaneous cognitive processes arising from
resting-state functional connectivity, the synergistic influence of cogni-
tive state is uncertain. More so than tasks, “ground truth”approaches
for pharmaco-imaging such as films (Meer et al., 2020) or other inte-
grated designs (Finn, 2021) paired with subject-level dynamical analy-
sis approaches (Shafiei et al., 2020) may hold promise in tagging the be-
havioural relevance of dFCs for attention-impairing drugs such as these.
4.2. Constituents of connectome self-identity are mutable under ayahuasca
Studies have repeatedly demonstrated the remarkable consistency
of inherent functional connectivity patterns across participants and
mental states (Gratton et al., 2018;Cole et al., 2014). Most of a subject's
uniqueness or inter-session variance (between 63 and 87%) can be ex-
plained by commonalities in functional connectivity architecture be-
tween states (Geerligs et al., 2015). Here, we show that contrary to a
phenomenological loss of self-identity, the degree of self-identifying
connectivity (Iself) is also preserved under ayahuasca. Psychedelics are
described to produce a wide-scale discoordination of brain activity (K.
H. Preller et al., 2018;Madsen et al., 2021) denoted by a structural-
functional uncoupling (Luppi et al., 2021) . To take the view that finger-
printing comprises fixed anatomical loci which assimilate several infor-
mation sources to plan coherent behavioural responses (Avena-
Koenigsberger et al., 2018) then their impaired integration as observed
under psychedelics should also lead to a diminished Iself. However, con-
nectome fingerprinting of clinical populations exhibiting structural-
functional uncoupling show no differences in Iself against healthy con-
trols (Stampacchia et al., 2022). Instead, it is now known the total blue-
print of functional connectivity does not constitute discrete networks
but is rather best described by more mutable local and global gradients
(Margulies and Smallwood, 2017) which are likely susceptible to phar-
macological perturbation. Indeed, our finding of diminished Iselfhybrid
may instead reflect a general functional reconfiguration of inherent sig-
nalling traits, synonymous with the apparition of a novel functional
connectivity architecture.
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4.3. Local shifts in functional connectivity stability drive altered
connectome fingerprints
At a fundamental level, we also looked at the edgewise contributors
to identification which might explain the observed reconfiguration of
identifiability. Previous work has closely implicated temporal stability
(as defined by ICC) of regional functional connectivity as driving con-
nectome fingerprints (Van De Ville et al., 2021). Using ICC, we ob-
served global reductions in edge stability at all measured timescales un-
der ayahuasca, mirroring the patterns of functional change under psy-
chedelics. 5-HT2A agonists have been found to produce brain-wide in-
creases in signal complexity (Varley et al., 2020;Viol et al., 2017).
which may consequently limit the temporal concordance of edge pairs.
Before continuing, it should be noted that functional connectivity and
ICC are not interchangeable but rather complementary methods (Noble
et al., 2021) and future work should continue to examine the mediating
relationship between the differing measures of complexity available,
ICC and connectome identifiability (Liu et al., 2020).
Given that certain edges drive a subject's normative fingerprint, we
examined how regional contributions to identifiability evolve under
ayahuasca. While a subset of 250 edges could maximally define a sub-
ject's fingerprint at baseline, their importance markedly dropped under
ayahuasca. Disseminated across higher-order association cortices, these
regions are shown to encode the majority of inter-individual variance
(Finn et al., 2015;Mantwill et al., 2022). Importantly, it has been previ-
ously hypothesised that the appearance of a desegregated functional ar-
chitecture under psychedelics stems from the impairment of these same
functional subsystems (M. K. Doss et al., 2021). However, frontotempo-
ral DMN nodes central to the effects of hallucinogens (Carhart-Harris et
al., 2014) emerged as the focal point for a subject's identifiability under
the influence, expressing greater stability. The DMN has been impli-
cated in different aspects of conscious experience, such as ongoing cog-
nition (Smallwood et al., 2021), spontaneous thought (Kam et al.,
2022), rumination (Chen et al., 2020), and self-referential processing
(Lebedev et al., 2016) and its select prevalence may further highlight a
diminished variability of subjective experience (Zamani et al., 2022) .
These shifts in stability were also pronounced at a network level.
While functional networks do not equally contribute to an individual's
fingerprint, each functional subsystem is thought to have temporal
“peaks'' in stability (Van De Ville et al., 2021;Mantwill et al., 2022).
Here, links pertaining to VIS and SM between-network connectivity ex-
hibited greater instability under ayahuasca, across all examined tempo-
ral windows. These findings appear in line with patterns of variable
connectivity in sensory networks that have been observed under psy-
chedelics (Preller et al., 2020;K. H. Preller et al., 2018), thought to re-
flect a dedifferentiation of hierarchical organisation (Girn et al., 2022).
Furthermore, clustering-based dFC approaches with LSD and psilocybin
have shown an increased fractional occurrence and dwell time of alter-
nating states of hyperconnectivity (Singleton et al., 2022;Olsen et al.,
2022;Lord et al., 2019)which may account for the stochasticity of edge
stability at each temporal window. It could be therefore suggested that
the outcome of a dedifferentiation of functional hierarchies under psy-
chedelics may also extend to their temporal organisation. Suggesting
this, both ends of the continuum of hierarchical organisation became
temporally more similar to one another under ayahuasca; with uni-
modal regions (eg. parietal operculum, visual cortex) associated with
rapid multisensory processing now peaking in stability at longer
timescales and transmodal regions (eg. prefrontal cortex, posterior cin-
gulate cortex) otherwise exhibiting longer, integrative firing patterns
maximal at shorter timescales. Applying approaches to examine the
temporal propagation and latency of brain activity (Raut et al., 2019;
Mitra et al., 2014) under psychedelics may highlight new explanations
for their effects on network architecture.
4.4. Connectome fingerprints are relevant to the subjective ayahuasca
experience
Assuming ayahuasca experiences are highly individual, might sub-
ject-level shifts in functional connectivity also help predict overlaying
subjective experiences? To explore this hypothesis, we devised a data
driven PCA approach to assess the behavioural relevance of highly
identifiable edges. Our results suggest that subsets of highly stable
edges not only drive a subject's identifiability under the influence but
also hold explanatory power for the AUD and VIS dimensions of the 5D-
ASC. In practice, by decomposing the total variance of a FC signal to a
reduced number of uncorrelated components, PCA offers the opportu-
nity to isolate maixmally behaviourally relevant FC motifs in a subset of
highly explanatory components. If group differences were highly ex-
planatory of experiential scores, then a single component (PC1) captur-
ing most of the sample variance might have emerged as a principal
model contributor. Instead, our results were contingent on the inclusion
of PC3 as a predictor of interest, suggesting higher-order PC deviations
capturing the sample heterogeneity in FC were most relevant to the vi-
sual and auditory effects of ayahuasca. Furthermore, predictive edges
were found to span primarily both higher-order systems (e.g., DMN)
and primary systems (e.g., VIS), with the former contributing strongly
to behavioural prediction as per prior work (Finn et al., 2015;da Silva
Castanheira et al., 2021;Mantwill et al., 2022). Given their develop-
mentally late maturation (Xia et al., 2022), susceptibility to individual
environmental effects (Valk et al., 2022), dense 5-HT2A expression
(Beliveau et al., 2017) and coordination of multisensory integration in
comparison to primary systems (Margulies and Smallwood, 2017),
higher-order regions may more easily account for divergent phenom-
ena, more so than primary systems, themselves partially influenced by
the temporary states of each individual during scanning (Agcaoglu et
al., 2019). While these proof-of-principle analyses are concordant with
more recent findings of behaviourally relevant connectome fingerprints
under psilocybin (Tolle et al., 2023), deriving reliable phenotypes from
resting state measures is contingent on large sample sizes (Liu et al.,
2023). Thus, replicating the present findings with an exhaustive PC
search space in a larger sample size will serve to not only define the reli-
ability but also the validity of these FC motifs as markers of subjective
experience.
4.5. Limitations
The present work comes with several limitations. Importantly,
members of Santo Daime are not reflective of the general population.
Drinking ayahuasca several times a month, members likely exhibit a
level of habituation to the drug's effect. Furthermore, 5-HT2A agonists
are potent psychoplastogens (De Vos et al., 2021) likely inducing struc-
tural alterations after prolonged use. For example, cortical thickness
analyses of Santo Daime members have demonstrated an association
between significant thinning in midline structures and self-
transcendent personality traits (Bouso et al., 2015). There is an abun-
dance of studies showing how between-subject differences in white
matter integrity are intimately related to interindividual variability in
functional dynamics (Genon et al., 2022;Gu et al., 2021) and likely
identifiability (Takao et al., 2015). As with observational studies, these
findings are subject to confounding effects regarding dosage, blinding,
sample inclusion criteria and expectancy. Adequate blinding in studies
comprising experienced users continues to be an unresolved factor in
the field, due to a subject's immediate recognition of a drug's effect (or
non-effect). This is further accentuated here, given the ritual elements.
Future studies with a diverse sample of ceremonial users may benefit
from not only counterbalancing and adequately blinding the agent in
question, but also surrounding practices mediating experiential out-
comes. With regard to the methodology, it is well-known that head mo-
tion due to its potential for skewing functional connectivity estimates
13

CORRECTED PROOF
P. Mallaroni et al. NeuroImage xxx (xxxx) 120480
(Power et al., 2014;Power et al., 2012) is likely a confounder in the
study of inter-individual differences (Finn et al., 2015). If treated as a
“state”characteristic for subjects, joint differences amongst members of
a group might account for a proportion of between-subject variability.
Whereas numerous steps were taken to exclude its influence, it is un-
known to what degree factors such as motion, respiratory fluctuations
or arousal level may prevail in shorter dFC windows. Future studies
should aim to replicate this workflow using framewise approaches such
as dynamic conditional correlations (Lindquist et al., 2014) or phase co-
herence estimation (Honari et al., 2021). Per prior work (Van De Ville
et al., 2021), pre-processing was performed with a pipeline comprising
Global Signal regression (GSR) given its capacity to improve the ex-
planatory value of resting-state FC for behaviour and abolish motion ar-
tifacts (Li et al., 2019). GS is hypothesised to contain a complex mixture
of non-neuronal artefacts (e.g., physiological, movement, scanner-
related) and its removal, while effective, is widely debated in light of
differing results for psychedelic effects due to the presence of anticorre-
lations (Palhano-Fontes et al., 2015;K. H. Preller et al., 2018;K. H.
Preller et al., 2018). While the present study sought instead to assess
the test-retest stability of functional connectomes and not their direc-
tionality, a consensus on the suitability of GSR for pharmaco-imaging
(McCulloch et al., 2022) and connectome fingerprinting should be
reached.
4.6. Conclusion
In summary, the ritualistic use of ayahuasca is associated with re-
duced connectome idiosyncrasy, marked by a spatiotemporal reconfig-
uration of brain connectivity traits. Members of Santo Daime pertain to
a culture which emphasises the interaction of a psychoactive sacrament
with the interpersonal dynamics of ritualism. Ultimately, it is possible
that the synergy of the two that produces the blurred connectome fin-
gerprint presented herein. An important next step is employing task de-
signs to directly assess the moderating role of interindividual differ-
ences for the variability of subjective experiences under psychedelics.
Going forwards, by celebrating individual differences in the study of
subjective experiences we may be a step closer to producing person-
alised neural markers of psychedelic effects.
Data availability
The connectomes and the accompanying covariates used to differen-
tiate individuals can be made available to qualified research institu-
tions upon reasonable request to J.G.R and a data use agreement exe-
cuted with Maastricht University.
Code availability
All code used for analysis is to be made available on P.Mallaroni's
GitHub page (https://github.com/PabloMallaroni)
CRediT authorship contribution statement
Pablo Mallaroni: Conceptualization, Investigation, Formal analy-
sis, Methodology, Data curation, Writing –original draft. Natasha L.
Mason: Conceptualization, Investigation, Supervision, Writing –re-
view & editing. Lilian Kloft: Conceptualization, Investigation, Writing
–review & editing. Johannes T. Reckweg: Conceptualization, Investi-
gation, Writing –review & editing. Kim van Oorsouw: Conceptualiza-
tion, Project administration, Resources. Stefan W. Toennes: Formal
analysis, Writing –review & editing. Hanna M. Tolle: Methodology,
Validation, Writing –review & editing. Enrico Amico: Conceptualiza-
tion, Methodology, Software, Supervision, Writing –review & editing.
Johannes G. Ramaekers: Conceptualization, Investigation, Data cura-
tion, Supervision, Project administration, Funding acquisition, Writing
–review & editing.
Declaration of Competing Interest
None of the authors declare any conflict of interest.
Data availability
Data will be made available on request.
Funding and disclosure
EA acknowledges financial support from the SNSF Ambizione pro-
ject "Fingerprinting the brain: network science to extract features of
cognition, behaviour and dysfunction" (grant number PZ00P2_185716).
JR acknowledges financial support from Dutch Research Council
(NWO) project “A targeted imaging-metabolomics approach to classify
harms of novel psychoactive substances”(grant number 406.18.
GO.019).
Acknowledgments
We would like to thank Gregory Cooper and Emanhuel Troizi Lopez
for insightful conversations on the nature of our findings. We are grate-
ful for the extended cooperation of members of the church of Santo
Daime.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.neuroimage.2023.120480.
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