INVESTIGACIÓN
Dynamic interactions between anterior insula and
anterior cingulate cortex link perceptual features
and heart rate variability during movie viewing
Saurabh Sonkusare1,2
, Katharina Wegner3, Catie Chang4, Sasha Dionisio5,6,
Michael Breakspear2,7, and Luca Cocchi2
1Department of Psychiatry, University of Cambridge, Cambridge, Reino Unido
2QIMR Berghofer Medical Research Institute, Brisbane, Australia
3Ghent University, Ghent, Bélgica
4Vanderbilt University, EE.UU
5The University of Queensland, Brisbane, Australia
6Advanced Epilepsy Unit, Mater Centre for Neurosciences, Mater Hospitals, Brisbane, Australia
7The University of Newcastle, Newcastle, Australia
Palabras clave: Connectivity, Neural dynamics, Movie, Heart rate, DCM, Emotions
ABSTRACTO
The dynamic integration of sensory and bodily signals is central to adaptive behaviour. A pesar de
the anterior cingulate cortex (CAC) and the anterior insular cortex (AIC) play key roles in this
proceso, their context-dependent dynamic interactions remain unclear. Aquí, we studied the
spectral features and interplay of these two brain regions using high-fidelity intracranial-EEG
recordings from five patients (CAC: 13 contacts, AIC: 14 contacts) acquired during movie viewing
with validation analyses performed on an independent resting intracranial-EEG dataset. ACC and
AIC both showed a power peak and positive functional connectivity in the gamma (30–35 Hz)
frequency while this power peak was absent in the resting data. We then used a neurobiologically
informed computational model investigating dynamic effective connectivity asking how it linked
to the movie’s perceptual (visual, audio) features and the viewer’s heart rate variability (HRV).
Exteroceptive features related to effective connectivity of ACC highlighting its crucial role in
processing ongoing sensory information. AIC connectivity was related to HRV and audio
emphasising its core role in dynamically linking sensory and bodily signals. Our findings provide
new evidence for complementary, yet dissociable, roles of neural dynamics between the ACC
and the AIC in supporting brain-body interactions during an emotional experience.
RESUMEN DEL AUTOR
Naturalistic stimulus such as movies provide an optimal platform to study time-resolved neural
interactions supporting integration of sensory and bodily information. Aquí, we obtain
intracranial recordings from anterior insular cortex and the anterior cingulate cortex while
Participantes (undergoing clinical evaluation for epilepsy) viewed a short movie capable of
eliciting robust physiological responses. We first characterise the spectral properties with
subsequent model driven approach to probe moment-to-moment neural interactions between
the anterior insular cortex and the anterior cingulate cortex. Our findings highlight how this
neural system plays a key role in linking the contextual integration of sensory information with
that of internal body status.
un acceso abierto
diario
Citación: Sonkusare, S., Wegner, K.,
Chang, C., Dionisio, S., romper la lanza, METRO.,
& cocineros, l. (2023). Dynamic
interactions between anterior insula
and anterior cingulate cortex link
perceptual features and heart rate
variability during movie viewing.
Neurociencia en red, 7(2), 557–577.
https://doi.org/10.1162/netn_a_00295
DOI:
https://doi.org/10.1162/netn_a_00295
Supporting Information:
https://doi.org/10.1162/netn_a_00295
Recibió: 3 Julio 2022
Aceptado: 17 Noviembre 2022
Conflicto de intereses: Los autores tienen
declaró que no hay intereses en competencia
existir.
Autor correspondiente:
Saurabh Sonkusare
sonkusaresaurabh@gmail.com
Editor de manejo:
Emily Finn
Derechos de autor: © 2023
Instituto de Tecnología de Massachusetts
Publicado bajo Creative Commons
Atribución 4.0 Internacional
(CC POR 4.0) licencia
La prensa del MIT
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Insula-cingulate dynamics
Interoception:
Registration, interpretación, y
integration of sensory signals from
within the body.
Heart rate variability:
Fluctuation in durations between
successive heart beats.
INTRODUCCIÓN
Brain activity continuously adapts to sensory inputs, triggering physiological responses to
threat and emotionally salient stimuli (Critchley, 2005; Damasio et al., 2000; menón, 2011).
The bidirectional coupling between external sensory cues and bodily physiological adapta-
tions represents a key function of the central nervous system. Sin embargo, the dynamic neural
processes supporting the integration of multimodal sensory and cognitive-emotional informa-
tion with somatic responses remain poorly understood.
The salience network, anatomically anchored in the dorsal anterior cingulate cortex (CAC)
and anterior insular cortex (AIC) (Seeley et al., 2007; Touroutoglou et al., 2012, 2016), tiene
been consistently implicated in the synthesis and integration of signals from the external envi-
ronment and the body (Critchley, 2005; Critchley et al., 2005; Seth, 2013; Uddin, 2015). Pre-
vious research suggests interoceptive signals from various bodily afferents are processed by the
posterior insula while the integration of high-level physiological and cognitive functions occur
anteriorly (Craig, 2009; Critchley, 2005; Critchley et al., 2005; Farb et al., 2013; Verano &
Parvizi, 2020; Nguyen et al., 2016; Simmons et al., 2013; tian & Brilla, 2018). The ACC
is believed to receive and integrate multisensory perceptual information and underpins diverse
cognitive functions, including emotion, motivación, and error monitoring (Ito et al., 2003;
Ullsperger & por cramon, 2001; Weston, 2012).
Unified accounts of the function of the ACC-AIC circuit suggest that the ACC supports the
cognitive appraisal of sensory information and the modulation of interoceptive representations
in the AIC (Bush et al., 2000; Hall et al., 2022; Harrison et al., 2015; Weston, 2012). The inte-
gration of multimodal information is especially relevant in emotional contexts which induce
changes in peripheral body signals such as heart rate (HR), respiration, and perspiration. Fur-
thermore, facial expressions appear to evoke activity in these regions, which are specific to the
emotion expressed, as observed in intracranial-EEG recordings (iEEG) (Bijanzadeh et al.,
2022). Functional magnetic resonance imaging (resonancia magnética funcional) studies using peripheral physiological
responses such as HR and skin conductance have demonstrated the role of the AIC-ACC dyad
in interoception (Gray et al., 2009; Nagai et al., 2004). Analyses of fMRI data have also sug-
gested that slow fluctuations (∼30 sec) in functional connectivity between the ACC and the
whole-brain covary with heart rate variability (HRV) (Chang et al., 2013). Sin embargo, el
limitations of fMRI prohibit analyses of rapid (<1 sec) neural computations underpinning
brain-body communication. High-fidelity recordings could overcome these challenges thus
unravelling the nature of coupling between ACC and AIC supporting the
dynamic process perceptual body signals.
To disentangle functional relevance dynamic patterns interactions the
AIC ACC, we used iEEG depth recordings. This invasive measure activity
has excellent spatiotemporal resolution compared to other neuroimaging techniques, includ-
ing fMRI. Moreover, is minimally affected by cardiorespiratory motion artefacts
(Parvizi & Kastner, 2018). We acquired from while
surgical participants (suffering epilepsy) watched an emotionally salient movie. This
allowed capturing not only induced dynamics but also accompanying physio-
logical response HRV evoked during ecologically valid sensory experience when com-
pared traditional task paradigms utilised in studies such as abstract and
static images (Sonkusare et al., 2019a, 2019b; see Grall Finn, 2022).
Using unique data, first sought understand power spectral features associated
with movie viewing well connectivity AIC. Furthermore, we
wanted characterise time-resolved changes feedforward feedback in
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>90% included feedforward connections (parámetros 3, 4, 11, 12) and feedback connections
(parámetros 5, 7, 8, 15, 16). Only these connectivity parameters were used for further analysis.
with >99.9% probability (Figure 5Bii and iii), which was used to model single-subject DCM
parámetros. A subsequent group-wise PEB analysis to identify the consistency of between-
window effects across the participants showed that model parameters incorporating both feed-
forward and backward connections reached a posterior probability of >99.9% (Figure 5B iv–vi).
These connectivity parameters were correlated with the movie features and HRV.
Feedforward and Feedback Connectivity Parameters Between the AIC and the ACC Are
Positively Correlated
We observed several significant relationships between connectivity parameters, HRV, and movie
características (Cifra 5 and Supporting Information Figure S2). The feedforward connectivity parame-
ters between AIC and ACC showed an inverse relationship (r= .13, pFDR = 8e−5), and feedback
Neurociencia en red
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Insula-cingulate dynamics
connectivity also showed a positive correlation (r= .13, pFDR = 4e−6). Note that weaker correla-
ciones (r < .1) are unlikely to be meaningful. However, for completeness these are shown in
Supporting Information Figure S2. We also undertook DCM and PEB analyses for resting-state
data. We found similar connections that showed between-window and between-subjects consis-
tency (Supporting Information Figure S1). Correlation analyses undertaken for these connectivity
parameters did not yield any significant associations (feedforward connectivity of AIC and that
of ACC (r = .1, p = .59), feedback connectivity of AIC and that of ACC (r = −.32, p = .09),
feedforward connectivity of AIC and feedback connectivity from ACC (r = −.28, p = .14), feed-
forward connectivity of ACC and feedback connectivity from AIC (r = −.25, p = .19)).
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Figure 6. Relationship between effective connectivity, heart rate variability (HRV), and movie fea-
tures. (A) Pearson’s correlation coefficient values (r) between connectivity parameters, HRV and
movie features. aiF = feedforward connections from the AIC to the ACC; aiB = feedback connec-
tions from the ACC to the AIC; acF = feedforward connections from ACC to AIC; acB = feedback
connections from the AIC to the ACC; lf/hf = ratio of low-frequency/high-frequency HRV; emo =
emotion scores; sal = salience; aud = audio; lum = luminance. Correlation coefficient values greater
than or equal to .10 that survive multiple comparison correction (FDR) with a type I error probability
of less than 5% are shown. See Supporting Information Figure S2 for all the correlation and prob-
ability values. See Supporting Information Figure S3 for effects of 8- and 10-sec smoothing on cor-
relation results (B) Simplified illustration of feedforward and feedback connectivity between AIC
and ACC and their association with data features. Connectivity parameters from the ACC linked
to stimuli-related sensory information, whereas AIC-driven connectivity was associated with both
the stimulus properties as well as physiological responses. Neuronal cells shown in panel B are
adapted from Moran et al. (2013).
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Association of AIC-ACC Connectivity With Physiological State and Movie Features
HRV. Feedforward connectivity from AIC to ACC showed negative correlation with LF/HF
ratio (r = −.10, pFDR = .001) (Figure 6A and B).
Movie features. Similarly, feedforward connectivity from ACC to AIC showed positive corre-
lation with facial emotion in the movie (r = .21, pFDR = 5e−13) and a negative correlation with
luminance (r = −.18, pFDR = 8e−10). Feedback connectivity of AIC (connection from ACC)
showed positive correlation with movie salience (r = .14, pFDR = 1e−5). Similarly feedback
connectivity of ACC (connection from AIC) showed significant positive correlation with facial
emotion in the movie (r = .10, pFDR = .001) and audio (r = .12, pFDR = 4e−5) (Figure 6A and B).
In sum, connectivity from AIC (feedforward and feedback) associated with HRV as well as
movie features (emotion and audio) while connectivity from ACC (feedforward and feedback)
was associated with emotion, salience, as well as luminance (Figure 6B).
Associations Between Movie Features and HRV
HRV showed a significant positive correlation with salience (r = .21, pFDR = 1e−12) and audio (r =
.21, pFDR = 2e−13). Emotion scores showed a significant negative correlation with luminance
(r = −.64, pFDR = 4e−135) a significant positive correlation with salience (r = .11, pFDR = 0.0002).
DISCUSSION
We study how the dynamic interplay between the ACC and the AIC supports the association
between multisensory brain and body responses. We found enhanced gamma neural activity
(30–35 Hz) in the ACC and the AIC during movie viewing, but not at rest, is strongly corre-
lated, suggesting a tight coupling between these two regions. Biophysical modelling extended
these findings by showing movie-specific patterns of effective connectivity between the ACC
and the AIC that link with sensory and somatic signals. Specifically, connectivity parameters
from the ACC linked to stimuli-related sensory information, whereas AIC-driven connectivity
was associated with both the stimulus properties as well as the physiological responses. These
results highlight the distinct, yet complementary, function of the dynamic neural coupling
between the AIC and the ACC to process sensory and body signals during a rich and dynamic
emotional experience.
Earlier studies mapped the power spectral profile of resting-state iEEG in both the AIC and
ACC activity, highlighting a beta peak in the AIC (Frauscher et al., 2018). We replicated these
findings and further showed that movie viewing induced gamma (30–35 Hz) activity in both
the ACC and the AIC. Similar activity has been detected during discrete task performance,
suggesting that gamma activity in the AIC-ACC circuit is ubiquitous to the active process of
integrating multisensory information (Bastin et al., 2016; Boroujeni et al., 2021).
Our connectivity findings are in line with the prevailing functional profile attributed to the
ACC (Craig, 2009; Critchley et al., 2004). Specifically, we found that feedback connectivity
from the ACC to the AIC is linked to external sensory information, in agreement with the
notion that the ACC supports the contextual appraisal of incoming sensory information
(Harrison et al., 2015). With its extensive patterns of anatomical and functional connectivity
with brain regions across all levels of the cortical hierarchy, the ACC seems optimally posi-
tioned to compute estimates of stimuli-driven contextual changes (Craig, 2009; Fan et al.,
2008; Medford & Critchley, 2010; Valentini, 2010). Accordingly, a number of neuroimaging
studies have provided support for such a role of ACC (Luu & Pederson, 2004; Maier & di
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Pellegrino, 2012). Our result of connectivity from ACC to AIC related to salience, emotion,
and luminance potentially highlights the rapid adaptation of ACC activity to changing external
properties. The ability to provide context to sensory information underpins core brain func-
tions that have been commonly ascribed to the ACC. These functions include error monitoring
(Holroyd & Coles, 2002; Ito et al., 2003; Ullsperger & von Cramon, 2001), cognitive control
(Cocchi et al., 2012, 2013; Dosenbach et al., 2006), emotional regulation (Gotlib et al., 2005;
Ramirez-Mahaluf et al., 2018), and generating and updating autonomic responses (Chang
et al., 2013; Critchley et al., 2003).
Time-resolved AIC connectivity parameters correlated with both emotion, audio and HRV
(Figure 4B), suggesting how neural signals generated by the AIC could likely convey both
exteroceptive and bodily physiological information. Previous neuroimaging findings support
this integrative role of the AIC (Critchley et al., 2005). Specifically, Nguyen et al. (2016)
showed that the AIC integrates exteroceptive audio signals encoded by the superior temporal
gyrus with interoceptive signals (HRV) processed by the posterior insula. Moreover, it has been
shown that respiratory training (via mindfulness) modulated AIC activity evoked by visual task
demands (Farb et al., 2013), highlighting a core integratory role of AIC.
Our findings are derived from the canonical microcircuit CMC DCM model, which has
been interpreted within a predictive coding framework. Herein, feedforward and feedback
connectivity carry prediction error and prediction signals, respectively (Bastos et al., 2012).
Our results suggest that predictions and prediction error’s conveyed between the AIC-ACC
dyad are positively correlated. These findings support the existence of a synergism underlying
the dynamic refining of the link between sensory stimuli properties and physiological
processes within the AIC-ACC system. Furthermore, connectivity from ACC to AIC, and its
association with exteroceptive movie properties (salience, audio, and luminance), point to a
key role of the ACC in continuously monitoring the fluctuating external environment. Feed-
back connectivity from ACC positively correlated with changes in visual stimuli salience, but
feedforward connectivity from ACC negatively correlated with luminance. In this context, and
as alluded to earlier about the role of ACC in generating autonomic responses, the functional
dissociation is in line with the notion that salient stimuli processing requires sympathetic con-
trol (i.e., pupil dilation; Kucyi & Parvizi, 2020), whereas the perceptual adaptation to stimulus’
luminance is under parasympathetic control (i.e., pupil constriction; Szabadi, 2018; Hess &
Polt, 1960). On the other hand, feedforward connectivity from AIC negatively correlated with
a proxy for sympatho-vagal balance (LF/HF ratio), suggesting the accumulation of predictions
errors and the need to update the internal homeostatic status (Owens et al., 2018). Together,
the observed patterns of connectivity and associations with stimulus properties imply that
iterative computations within the AIC-ACC system play a key role in minimising the overall
surprise to the ongoing stream of information. More generally, the casting of our findings
within the predictive coding framework extends knowledge on the neural mechanisms linking
exteroceptive sensory and body responses (Seth, 2013).
Several limitations need to be considered when interpreting our findings. Despite the rich-
ness and quality of the data, it comes from patients with epilepsy. Epilepsy is increasingly seen
as a network disorder affecting multiple regions (Bernhardt et al., 2015; Kanner et al., 2017).
Some patients had epileptic zones in structurally connected region (e.g., amygdala) or homol-
ogous contralateral regions. Although we undertook extensive quality control, residual
epilepsy-related artefacts and or pathological activity propagating to the AIC or ACC may none-
theless influence movie-related AIC-ACC circuit activity. Furthermore, the results are based on
five individuals in the movie condition. Stereo-EEG clinics remain relatively rare, and access to
data for nonclinical research is challenging. In this study, colocation of electrode channels at
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two regions of interest further constrained the sample size. With unique implantation schemes
for each patient, large sample sizes will be required to replicate and extend our findings. In
addition, slight variations in the contact locations could also influence the results. However,
our results are based on contacts located in relatively small and spatially constrained cortical
regions that have been previously demonstrated to serve similar functions (Kolling et al., 2016;
Medford & Critchley, 2010; Tian et al., 2019). A granular coverage at the millimetre scale is
needed to explore possible spatial specificity of AIC and ACC connectivity.
Another limitation is that most of the contacts were located on the right hemisphere, with
only one patient contributing contacts from the left hemisphere. Structural and functional
asymmetry of AIC and ACC have been reported (Biduła & Króliczak, 2015; Chiarello et al.,
2016; Yan et al., 2009) and thus, further research is needed to explore the possible functional
differences of left and right AIC-ACC activity and connectivity. For our effective connectivity
analyses, we used the CMC biophysical model. While this model incorporates the general
organisation of cortical circuitry (Felleman & Van Essen, 1991), specific differences in the
cytoarchitecture of AIC and ACC may affect the quantification of connectivity parameters.
Concerning the temporal trajectories employed in our PEB analyses, each connectivity
parameter’s time course was modelled by a linear combination of six temporal basis functions.
Although these linear combinations cover a wide range of plausible temporal trajectories, we
cannot exclude that more complex (e.g., sinusoidal) trajectories capture additional movie-
induced neural dynamics within the AIC-ACC system. We also choose a long smoothing
window for our correlational analyses to capture the long temporal scales of high-order asso-
ciative regions including the ACC and AI (Honey et al., 2012) as well as the slow nature of
HRV (∼0.1 Hz). Supplementary analysis did, however, show that results were robust to differ-
ent smoothing window lengths of 8 and 12 sec (Supporting Information Figure S3).
We also note that the subjective engagement during movie viewing could not be quantified
due to the time constraints in the surgical unit. However, we did ensure that participants
remained vigilant while watching the movie by continuous monitoring via a wall-mounted
camera. Moreover, the quantification of emotional profile of stimulus was based on five negative
and one positive valence categories of facial expressions of actors in the movie. Thus, we were
unable to investigate possible idiosyncrasies supporting the processing of stimuli with positive
and negative emotional valence. We additionally used automated facial emotion expression.
Supervised learning algorithms classifying basic facial expressions based on feature values were
previously variable but has recently achieved accuracies of 75–98% when benchmarked against
manually coded datasets of both posed and spontaneous expressions (Ekundayo & Viriri, 2019).
Finally, future studies are required to expand on our analyses by incorporating a wider repertoire
of bodily signals, including galvanic skin response and facial expressions.
The characterisation of neural dynamics within the AIC-ACC system has important impli-
cations for the study of brain disorders (Menon, 2011). For example, several neuroimaging
studies have demonstrated exacerbated stimuli-induced activity and connectivity between
the AIC and the ACC in anxiety disorders and obsessive-compulsive disorders (Cocchi
et al., 2012; Paulus & Stein, 2006, 2010; Peterson et al., 2014; Pujol et al., 1999; Simmons
et al., 2013). Our findings provide new testable hypotheses on the nature of these deficits that
may facilitate the development of new targeted therapeutic interventions.
ACKNOWLEDGMENTS
We thank Annett Koenig for her assistance in arrangements for data acquisition. We thank
Kartik Iyer and Caitlin Hall for helpful discussions.
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SUPPORTING INFORMATION
Supporting information for this article is available at https://doi.org/10.1162/netn_a_00295.
The data for this project were acquired from clinical epilepsy participants undergoing clinical
care and consenting for additional research protocols. Local ethics approval mandated strict
privacy restrictions around their availability outside of the named investigator team.
Researchers wishing to access these data will require local ethics approval and a data sharing
agreement with QIMR Berghofer and Mater Hospital Brisbane.
AUTHOR CONTRIBUTIONS
Saurabh Sonkusare: Conceptualization; Data curation; Formal analysis; Investigation; Method-
ology; Project administration; Resources; Software; Visualization; Writing – original draft;
Writing – review & editing. Katharina Wegner: Formal analysis; Methodology; Writing –
review & editing. Catie Chang: Methodology; Supervision; Writing – review & editing. Sasha
Dionisio: Methodology; Resources; Writing – review & editing. Michael Breakspear: Concep-
tualization; Funding acquisition; Investigation; Project administration; Supervision; Writing –
original draft; Writing – review & editing. Luca Cocchi: Conceptualization; Funding acquisition;
Supervision; Writing – original draft; Writing – review & editing.
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FUNDING INFORMATION
Michael Breakspear, QIMR Berghofer Medical Research Institute (https://dx.doi.org/10.13039
/100013103), Award ID: 6626. Luca Cocchi, Australian National Health Medical Research
Council, Award ID: GN2001283.
REFERENCES
Allen, J. J., Chambers, A. S., & Towers, D. N. (2007). The many metrics
of cardiac chronotropy: A pragmatic primer and a brief comparison
of metrics. Biological Psychology, 74(2), 243–262. https://doi.org
/10.1016/j.biopsycho.2006.08.005, PubMed: 17070982
Ashburner, J., Barnes, G., Chen, C.-C., Daunizeau, J., Flandin, G.,
Friston, K., … Zeidman, P. (2014). SPM12 manual. London, UK:
Wellcome Trust Centre for Neuroimaging.
Auksztulewicz, R., & Friston, K. (2015). Attentional enhancement
of auditory mismatch responses: A DCM/MEG study. Cerebral
Cortex, 25(11), 4273–4283. https://doi.org/10.1093/cercor
/bhu323, PubMed: 25596591
Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., &
Norman, K. A. (2017). Discovering event structure in continuous
narrative perception and memory. Neuron, 95(3), 709–721.
https://doi.org/10.1016/j.neuron.2017.06.041, PubMed: 28772125
Barbas, H., & Rempel-Clower, N. (1997). Cortical structure predicts
the pattern of corticocortical connections. Cerebral Cortex, 7(7),
635–646. https://doi.org/10.1093/cercor/7.7.635, PubMed:
9373019
Bastin, J., Deman, P., David, O., Gueguen, M., Benis, D., Minotti,
L., … Perrone-Bertolotti, M. (2016). Direct recordings from
human anterior insula reveal its leading role within the
error-monitoring network. Cerebral Cortex, 27(2), 1545–1557.
https://doi.org/10.1093/cercor/bhv352, PubMed: 26796212.
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P.,
& Friston, K. J. (2012). Canonical microcircuits for predictive
coding. Neuron, 76(4), 695–711. https://doi.org/10.1016/j
.neuron.2012.10.038, PubMed: 23177956
Bernhardt, B. C., Bonilha, L., & Gross, D. W. (2015). Network anal-
ysis for a network disorder: The emerging role of graph theory in
the study of epilepsy. Epilepsy & Behavior, 50, 162–170. https://
doi.org/10.1016/j.yebeh.2015.06.005, PubMed: 26159729
Biduła, S. P., & Króliczak, G. (2015). Structural asymmetry of the
insula is linked to the lateralization of gesture and language.
European Journal of Neuroscience, 41(11), 1438–1447. https://
doi.org/10.1111/ejn.12888, PubMed: 25858359
Bijanzadeh, M., Khambhati, A. N., Desai, M., Wallace, D. L., Shafi,
A., Dawes, H. E., … Chang, E. F. (2022). Decoding naturalistic
affective behaviour from spectro-spatial features in multiday
human iEEG. Nature Human Behaviour, 6(6), 823–836. https://
doi.org/10.1038/s41562-022-01310-0, PubMed: 35273355
Billman, G. E. (2013). The effect of heart rate on the heart rate
variability response to autonomic interventions. Frontiers in
Physiology, 4, 222. https://doi.org/10.3389/fphys.2013.00222,
PubMed: 23986716
Boiten, F. A., Frijda, N. H., & Wientjes, C. J. (1994). Emotions and
respiratory patterns: review and critical analysis. International
Journal of Psychophysiology, 17(2), 103–128. https://doi.org/10
.1016/0167-8760(94)90027-2, PubMed: 7995774
Boroujeni, K. B., Tiesinga, P., & Womelsdorf, T. (2021). Interneuron-
specific gamma synchronization indexes cue uncertainty and
prediction errors in lateral prefrontal and anterior cingulate
Network Neuroscience
573
Insula-cingulate dynamics
cortex. Elife, 10, e69111. https://doi.org/10.7554/eLife.69111,
PubMed: 34142661
Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional
influences in anterior cingulate cortex. Trends in Cognitive Sci-
ence, 4(6), 215–222. https://doi.org/10.1016/s1364-6613(00)
01483-2, PubMed: 10827444
Camm, J. (1996). Task Force of the European Society of Cardiology
and the North American Society of Pacing and Electrophysiology.
Heart Rate Variability: Standards of measurement, physiological
interpretation and clinical use. Circulation, 93, 1043–1065.
https://doi.org/10.1161/01.CIR.93.5.1043, PubMed: 8598068
Chang, C., Metzger, C. D., Glover, G. H., Duyn, J. H., Heinze,
H.-J., & Walter, M. (2013). Association between heart rate vari-
ability and fluctuations in resting-state functional connectivity.
NeuroImage, 68, 93–104. https://doi.org/10.1016/j.neuroimage
.2012.11.038, PubMed: 23246859
Chiarello, C., Vazquez, D., Felton, A., & McDowell, A. (2016).
Structural asymmetry of the human cerebral cortex: Regional
and between-subject variability of surface area, cortical thickness,
and local gyrification. Neuropsychologia, 93(Pt B), 365–379.
https://doi.org/10.1016/j.neuropsychologia.2016.01.012,
PubMed: 26792368
Cocchi, L., Harrison, B. J., Pujol, J., Harding, I. H., Fornito, A., Pantelis,
C., & Yücel, M. (2012). Functional alterations of large-scale brain
networks related to cognitive control in obsessive-compulsive
disorder. Human Brain Mapping, 33(5), 1089–1106. https://doi
.org/10.1002/hbm.21270, PubMed: 21612005
Cocchi, L., Zalesky, A., Fornito, A., & Mattingley, J. B. (2013).
Dynamic cooperation and competition between brain systems
during cognitive control. Trends in Cognitive Science, 17(10),
493–501. https://doi.org/10.1016/j.tics.2013.08.006, PubMed:
24021711
Craig, A. D. B. (2009). How do you feel–now? The anterior insula
and human awareness. Nature Reviews Neuroscience, 10(1),
59–70. https://doi.org/10.1038/nrn2555, PubMed: 19096369
Critchley, H. D. (2005). Neural mechanisms of autonomic, affec-
tive, and cognitive integration. Journal of Comparative Neurol-
ogy, 493(1), 154–166. https://doi.org/10.1002/cne.20749,
PubMed: 16254997
Critchley, H. D., Mathias, C. J., Josephs, O., O’Doherty, J., Zanini,
S., Dewar, B. K., … Dolan, R. J. (2003). Human cingulate cortex
and autonomic control: Converging neuroimaging and clinical
evidence. Brain, 126(Pt 10), 2139–2152. https://doi.org/10
.1093/brain/awg216, PubMed: 12821513
Critchley, H. D., Rotshtein, P., Nagai, Y., O’Doherty, J., Mathias,
C. J., & Dolan, R. J. (2005). Activity in the human brain predicting
differential heart rate responses to emotional facial expressions.
NeuroImage, 24(3), 751–762. https://doi.org/10.1016/j
.neuroimage.2004.10.013, PubMed: 15652310
Critchley, H. D., Wiens, S., Rotshtein, P., Ohman, A., & Dolan, R. J.
(2004). Neural systems supporting interoceptive awareness.
Nature Neuroscience, 7(2), 189–195. https://doi.org/10.1038
/nn1176, PubMed: 14730305
Damasio, A. R., Grabowski, T. J., Bechara, A., Damasio, H., Ponto,
L. L., Parvizi, J., & Hichwa, R. D. (2000). Subcortical and cortical
brain activity during the feeling of self-generated emotions.
Nature Neuroscience, 3(10), 1049–1056. https://doi.org/10
.1038/79871, PubMed: 11017179
Das, A., & Menon, V. (2020). Spatiotemporal integrity and sponta-
neous nonlinear dynamic properties of the salience network
revealed by human intracranial electrophysiology: A multicohort
replication. Cerebral Cortex, 30(10), 5309–5321. https://doi.org
/10.1093/cercor/bhaa111, PubMed: 32426806
Del Sole, A. (2018). Introducing Microsoft cognitive services. In
Microsoft computer vision APIs distilled (pp. 1–4). Berlin:
Springer. https://doi.org/10.1007/978-1-4842-3342-9_1
Delorme, A., & Makeig, S. (2004). EEGLAB: An open source tool-
box for analysis of single-trial EEG dynamics including indepen-
dent component analysis. Journal of Neuroscience Methods,
134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009,
PubMed: 15102499
Donoghue, T., Haller, M., Peterson, E. J., Varma, P., Sebastian, P.,
Gao, R., … Knight, R. T. (2020). Parameterizing neural power
spectra into periodic and aperiodic components. Nature Neuro-
science, 23(12), 1655–1665. https://doi.org/10.1038/s41593-020
-00744-x, PubMed: 33230329
Dosenbach, N. U., Visscher, K. M., Palmer, E. D., Miezin, F. M.,
Wenger, K. K., Kang, H. C., … Petersen, S. E. (2006). A core sys-
tem for the implementation of task sets. Neuron, 50(5), 799–812.
https://doi.org/10.1016/j.neuron.2006.04.031, PubMed:
16731517
Ekundayo, O., & Viriri, S. (2019). Facial expression recognition: A
review of methods, performances and limitations. In 2019 Con-
ference on Information Communications Technology and Society
(ICTAS) (pp. 1–6). https://doi.org/10.1109/ICTAS.2019.8703619
Fan, J., Hof, P. R., Guise, K. G., Fossella, J. A., & Posner, M. I.
(2008). The functional integration of the anterior cingulate cortex
during conflict processing. Cerebral Cortex, 18(4), 796–805.
https://doi.org/10.1093/cercor/bhm125, PubMed: 17652463
Farb, N. A., Segal, Z. V., & Anderson, A. K. (2013). Attentional
modulation of primary interoceptive and exteroceptive cortices.
Cerebral Cortex, 23(1), 114–126. https://doi.org/10.1093/cercor
/bhr385, PubMed: 22267308
Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical
processing in the primate cerebral cortex. Cerebral Cortex, 1(1),
1–47. https://doi.org/10.1093/cercor/1.1.1-a, PubMed: 1822724
Frauscher, B., von Ellenrieder, N., Zelmann, R., Doležalová, I.,
Minotti, L., Olivier, A., … Gotman, J. (2018). Atlas of the normal
intracranial electroencephalogram: Neurophysiological awake
activity in different cortical areas. Brain, 141(4), 1130–1144.
https://doi.org/10.1093/brain/awy035, PubMed: 29506200
Friston, B. A., Litvak, V., Stephan, K. E., Fries, P., & Moran, R. J.
(2012). DCM for complex-valued data: Cross-spectra, coherence
and phase-delays. NeuroImage, 59(1), 439–455. https://doi.org
/10.1016/j.neuroimage.2011.07.048, PubMed: 21820062
Friston, K. J. (2011). Functional and effective connectivity: A
review. Brain Connectivity, 1(1), 13–36. https://doi.org/10.1089
/brain.2011.0008, PubMed: 22432952
Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal
modelling. NeuroImage, 19(4), 1273–1302. https://doi.org/10
.1016/s1053-8119(03)00202-7, PubMed: 12948688
Gotlib, I. H., Sivers, H., Gabrieli, J. D., Whitfield-Gabrieli, S.,
Goldin, P., Minor, K. L., & Canli, T. (2005). Subgenual anterior
cingulate activation to valenced emotional stimuli in major
depression. Neuroreport, 16(16), 1731–1734. https://doi.org/10
.1097/01.wnr.0000183901.70030.82, PubMed: 16237317
Network Neuroscience
574
l
D
o
w
n
o
a
d
e
d
f
r
o
m
h
t
t
p
:
/
/
d
i
r
e
c
t
.
m
i
t
.
/
t
/
e
d
u
n
e
n
a
r
t
i
c
e
-
p
d
l
f
/
/
/
/
/
7
2
5
5
7
2
1
1
8
5
1
8
n
e
n
_
a
_
0
0
2
9
5
p
d
.
t
f
b
y
g
u
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Insula-cingulate dynamics
Grall, C., & Finn, E. S. (2022). Leveraging the power of media to drive
cognition: A media-informed approach to naturalistic neuroscience.
Social Cognitive and Affective Neuroscience, 17(6), 598–608.
https://doi.org/10.1093/scan/nsac019, PubMed: 35257180
Gray, M. A., Rylander, K., Harrison, N. A., Wallin, B. G., &
Critchley, H. D. (2009). Following one’s heart: Cardiac rhythms
gate central initiation of sympathetic reflexes. Journal of Neuro-
science, 29(6), 1817–1825. https://doi.org/10.1523/JNEUROSCI
.3363-08.2009, PubMed: 19211888
Hall, C. V., Harrison, B. J., Iyer, K. K., Savage, H. S., Zakrzewski,
M., Simms, L. A., … Cocchi, L. (2022). Microbiota links to neural
dynamics supporting threat processing. Human Brain Mapping,
43(2), 733–749. https://doi.org/10.1002/hbm.25682, PubMed:
34811847
Harrison, B. J., Fullana, M. A., Soriano-Mas, C., Via, E., Pujol, J.,
Martínez-Zalacaín, I., … Cardoner, N. (2015). A neural mediator
of human anxiety sensitivity. Human Brain Mapping, 36(10),
3950–3958. https://doi.org/10.1002/ hbm.22889, PubMed:
26147233
Hess, E. H., & Polt, J. M. (1960). Pupil size as related to interest
value of visual stimuli. Science, 132(3423), 349–350. https://
doi.org/10.1126/science.132.3423.349, PubMed: 14401489
Hilgetag, C. C., Burns, G. A., O’Neill, M. A., Scannell, J. W., &
Young, M. P. (2000). Anatomical connectivity defines the organi-
zation of clusters of cortical areas in the macaque monkey and
the cat. Philosophical Transactions of the Royal Society of Lon-
don. Series B: Biological Sciences, 355(1393), 91–110. https://
doi.org/10.1098/rstb.2000.0551, PubMed: 10703046
Holroyd, C. B., & Coles, M. G. H. (2002). The neural basis of
human error processing: Reinforcement learning, dopamine,
and the error-related negativity. Psychology Reviews, 109(4),
679–709. https://doi.org/10.1037/0033-295x.109.4.679,
PubMed: 12374324
Honey, C. J., Thesen, T., Donner, T. H., Silbert, L. J., Carlson, C. E.,
Devinsky, O., … Hasson, U. (2012). Slow cortical dynamics and
the accumulation of information over long timescales. Neuron,
76(2), 423–434. https://doi.org/10.1016/j.neuron.2012.08.011,
PubMed: 23083743
Ito, S., Stuphorn, V., Brown, J. W., & Schall, J. D. (2003). Perfor-
mance monitoring by the anterior cingulate cortex during sac-
cade countermanding. Science, 302(5642), 120–122. https://doi
.org/10.1126/science.1087847, PubMed: 14526085
Jerbi, K., Ossandon, T., Hamame, C. M., Senova, S., Dalal, S. S.,
Jung, J., … Kahane, P. (2009). Task-related gamma-band dynamics
from an intracerebral perspective: Review and implications for sur-
face EEG and MEG. Human Brain Mapping, 30(6), 1758–1771.
https://doi.org/10.1002/hbm.20750, PubMed: 19343801
Jönsson, P., & Sonnby-Borgström, M. (2003). The effects of pictures
of emotional faces on tonic and phasic autonomic cardiac control
in women and men. Biological Psychology, 62(2), 157–173.
https://doi.org/10.1016/S0301-0511(02)00114-X, PubMed:
12581690
Kanner, A. M., Scharfman, H., Jette, N., Anagnostou, E., Bernard,
C., Camfield, C., … Giacobbe, P. (2017). Epilepsy as a network
disorder (1): What can we learn from other network disorders
such as autistic spectrum disorder and mood disorders? Epilepsy
& Behavior, 77, 106–113. https://doi.org/10.1016/j.yebeh.2017
.09.014, PubMed: 29107450
Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008). A hierarchy of
time-scales and the brain. PLoS Computational Biology, 4(11),
e1000209. https://doi.org/10.1371/journal.pcbi.1000209,
PubMed: 19008936
Kolling, N., Behrens, T., Wittmann, M., & Rushworth, M. (2016).
Multiple signals in anterior cingulate cortex. Current Opinion
in Neurobiology, 37, 36–43. https://doi.org/10.1016/j.conb
.2015.12.007, PubMed: 26774693
Kucyi, A., & Parvizi, J. (2020). Pupillary dynamics link spontaneous
and task-evoked activations recorded directly from human
insula. Journal of Neuroscience, 40(32), 6207–6218. https://doi
.org/10.1523/JNEUROSCI.0435-20.2020, PubMed: 32631937
Lachaux, J. P., Rudrauf, D., & Kahane, P. (2003). Intracranial EEG
and human brain mapping. Journal of Physiology-Paris, 97(4–6),
613–628. https://doi.org/10.1016/j.jphysparis.2004.01.018,
PubMed: 15242670
Li, N., Bi, H., Zhang, Z., Kong, X., & Lu, D. (2018). Performance
comparison of saliency detection. Advances in Multimedia,
2018, 9497083. https://doi.org/10.1155/2018/9497083
Luck, S. J. (2014). An introduction to the event-related potential
technique. Cambridge, MA: MIT Press.
Luu, P., & Pederson, S. M. (2004). The anterior cingulate cortex:
Regulating actions in context. In M. I. Posner (Ed.), Cognitive
neuroscience of attention (pp. 232–242). New York: Guilford
Publication, Inc.
Maier, M. E., & di Pellegrino, G. (2012). Impaired conflict adapta-
tion in an emotional task context following rostral anterior cingu-
late cortex lesions in humans. Journal of Cognitive Neuroscience,
24(10), 2070–2079. https://doi.org/10.1162/jocn_a_00266,
PubMed: 22721382
McNamara, Q., De La Vega, A., & Yarkoni, T. (2017). Developing a
comprehensive framework for multimodal feature extraction. In
Proceedings of the 23rd ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining (pp. 1567–1574).
https://doi.org/10.1145/3097983.3098075
Medford, N., & Critchley, H. D. (2010). Conjoint activity of anterior
insular and anterior cingulate cortex: Awareness and response.
Brain Structure and Function, 214(5–6), 535–549. https://doi
.org/10.1007/s00429-010-0265-x, PubMed: 20512367
Meer, J. N. van der, Breakspear, M., Chang, L. J., Sonkusare, S., &
Cocchi, L. (2020). Movie viewing elicits rich and reliable brain
state dynamics. Nature Communications, 11(1), 5004. https://doi
.org/10.1038/s41467-020-18717-w, PubMed: 33020473
Menon, V. (2011). Large-scale brain networks and psychopathology:
A unifying triple network model. Trends in Cognitive Science,
15(10), 483–506. https://doi.org/10.1016/j.tics.2011.08.003,
PubMed: 21908230
Michelmann, S., Treder, M. S., Griffiths, B., Kerrén, C., Roux, F.,
Wimber, M., … Gollwitzer, S. (2018). Data-driven re-referencing
of intracranial EEG based on independent component analysis
(ICA). Journal of Neuroscience Methods, 307, 125–137. https://
doi.org/10.1016/j.jneumeth.2018.06.021, PubMed: 29960028
Moran, R., Pinotsis, D. A., & Friston, K. (2013). Neural masses and
fields in dynamic causal modeling. Frontiers in Computational
Neuroscience, 7, 57. https://doi.org/10.3389/fncom.2013
.00057, PubMed: 23755005
Nagai, Y., Critchley, H. D., Featherstone, E., Trimble, M. R., &
Dolan, R. J. (2004). Activity in ventromedial prefrontal cortex
Network Neuroscience
575
l
D
o
w
n
o
a
d
e
d
f
r
o
m
h
t
t
p
:
/
/
d
i
r
e
c
t
.
m
i
t
.
t
/
/
e
d
u
n
e
n
a
r
t
i
c
e
-
p
d
l
f
/
/
/
/
/
7
2
5
5
7
2
1
1
8
5
1
8
n
e
n
_
a
_
0
0
2
9
5
p
d
.
t
f
b
y
g
u
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Insula-cingulate dynamics
covaries with sympathetic skin conductance level: A physiolog-
ical account of a “default mode” of brain function. NeuroImage,
22(1), 243–251. https://doi.org/10.1016/j.neuroimage.2004.01
.019, PubMed: 15110014
Nguyen, V. T., Breakspear, M., Hu, X., & Guo, C. C. (2016). The
integration of the internal and external milieu in the insula
during dynamic emotional experiences. NeuroImage, 124(Pt A),
455–463. https://doi.org/10.1016/j.neuroimage.2015.08.078,
PubMed: 26375211
Nguyen, V. T., Sonkusare, S., Stadler, J., Hu, X., Breakspear, M., &
Guo, C. C. (2017). Distinct cerebellar contributions to cognitive-
perceptual dynamics during natural viewing. Cerebral Cortex,
27(12), 5652–5662. https://doi.org/10.1093/cercor/bhw334,
PubMed: 29145671
Nichols, T., & Hayasaka, S. (2003). Controlling the familywise error
rate in functional neuroimaging: A comparative review. Statistical
Methods in Medical Research, 12(5), 419–446. https://doi.org/10
.1191/0962280203sm341ra, PubMed: 14599004
Owens, A. P., Allen, M., Ondobaka, S., & Friston, K. J. (2018). Inter-
oceptive inference: From computational neuroscience to clinic.
Neuroscience and Biobehavioral Reviews, 90, 174–183. https://
doi.org/10.1016/j.neubiorev.2018.04.017, PubMed: 29694845
Pagani, M., Lombardi, F., Guzzetti, S., Rimoldi, O., Furlan, R.,
Pizzinelli, P., … Piccaluga, E. (1986). Power spectral analysis of
heart rate and arterial pressure variabilities as a marker of
sympatho-vagal interaction in man and conscious dog. Circula-
tion Research, 59(2), 178–193. https://doi.org/10.1161/01.RES.59
.2.178, PubMed: 2874900
Pagani, M., Lombardi, F., Guzzetti, S., Sandrone, G., Rimoldi, O.,
Malfatto, G., … Malliani, A. (1984). Power spectral density of
heart rate variability as an index of sympatho-vagal interaction
in normal and hypertensive subjects. Journal of Hypertension,
2(3), S383–S385. PubMed: 6599685
Palva, J. M., Palva, S., & Kaila, K. (2005). Phase synchrony among
neuronal oscillations in the human cortex. Journal of Neurosci-
ence, 25(15), 3962–3972. https://doi.org/10.1523/JNEUROSCI
.4250-04.2005, PubMed: 15829648
Parvizi, J., & Kastner, S. (2018). Promises and limitations of human
intracranial electroencephalography. Nature Neuroscience, 21(4),
474–483. https://doi.org/10.1038/s41593-018-0108-2, PubMed:
29507407
Paulus, M. P., & Stein, M. B. (2006). An insular view of anxiety.
Biology and Psychiatry, 60(4), 383–387. https://doi.org/10.1016
/j.biopsych.2006.03.042, PubMed: 16780813
Paulus, M. P., & Stein, M. B. (2010). Interoception in anxiety and
depression. Brain Structure and Function, 214(5–6), 451–463.
https://doi.org/10.1007/s00429-010-0258-9 , PubMed:
20490545
Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J., & Nichols,
T. E. (2011). Statistical parametric mapping: The analysis of func-
tional brain images. Berlin: Elsevier.
Peterson, A., Thome, J., Frewen, P., & Lanius, R. A. (2014). Resting-
state neuroimaging studies: A new way of identifying differences
and similarities among the anxiety disorders? Canadian Journal
of Psychiatry, 59(6), 294–300. https://doi.org/10.1177
/070674371405900602, PubMed: 25007403
Pinotsis, D. A., Moran, R. J., & Friston, K. J. (2012). Dynamic causal
modeling with neural fields. NeuroImage, 59(2), 1261–1274.
https://doi.org/10.1016/j.neuroimage.2011.08.020, PubMed:
21924363
Potes, C., Gunduz, A., Brunner, P., & Schalk, G. (2012). Dynamics
of electrocorticographic (ECoG) activity in human temporal and
frontal cortical areas during music listening. NeuroImage, 61(4),
841–848. https://doi.org/10.1016/j.neuroimage.2012.04.022,
PubMed: 22537600
Pujol, J., Torres, L., Deus, J., Cardoner, N., Pifarré, J., Capdevila, A.,
& Vallejo, J. (1999). Functional magnetic resonance imaging
study of frontal lobe activation during word generation in
obsessive-compulsive disorder. Biology and Psychiatry, 45(7),
891–897. https://doi.org/10.1016/s0006-3223(98)00099-7,
PubMed: 10202577
Ramirez-Mahaluf, J. P., Perramon, J., Otal, B., Villoslada, P., &
Compte, A. (2018). Subgenual anterior cingulate cortex controls
sadness-induced modulations of cognitive and emotional net-
work hubs. Scientific Reports, 8(1), 8566. https://doi.org/10
.1038/s41598-018-26317-4, PubMed: 29867204
Ramshur, J. T. (2010). Design, evaluation, and application of heart
rate variability analysis software (HRVAS). University of Memphis,
Memphis, TN.
Ravenswaaij-Arts, C. M., Kollée, L. A., Hopman, J. C., Stoelinga,
G. B., & van Geijn, H. P. (1993). Heart rate variability: Standards
of measurement, physiologic interpretation, and clinical use.
Annals of Internal Medicine, 118(6), 436–447. https://doi.org/10
.7326/0003-4819-118-6-199303150-00008, PubMed: 8439119
Ren, Y., Nguyen, V. T., Sonkusare, S., Lv, J., Pang, T., Guo, L., …
Guo, C. C. (2018). Effective connectivity of the anterior hippo-
campus predicts recollection confidence during natural memory
retrieval. Nature Communications, 9(1), 4875. https://doi.org/10
.1038/s41467-018-07325-4, PubMed: 30451864
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H.,
Kenna, H., … Greicius, M. D. (2007). Dissociable intrinsic
connectivity networks for salience processing and executive
control. Journal of Neuroscience, 27(9), 2349–2356. https://doi
.org/10.1523/JNEUROSCI.5587-06.2007, PubMed: 17329432
Seth, A. K. (2013). Interoceptive inference, emotion, and the
embodied self. Trends in Cognitive Science, 17(11), 565–573.
https://doi.org/10.1016/j.tics.2013.09.007, PubMed: 24126130
Shaffer, F., McCraty, R., & Zerr, C. L. (2014). A healthy heart is not a
metronome: An integrative review of the heart’s anatomy and
heart rate variability. Frontiers in Psychology, 5, 1040. https://
doi.org/10.3389/fpsyg.2014.01040, PubMed: 25324790
Shirhatti, V., Borthakur, A., & Ray, S. (2016). Effect of reference
scheme on power and phase of the local field potential. Neural
Computation, 28(5), 882–913. https://doi.org/10.1162/NECO_a
_00827, PubMed: 26942748
Simmons, W. K., Avery, J. A., Barcalow, J. C., Bodurka, J., Drevets,
W. C., & Bellgowan, P. (2013). Keeping the body in mind: Insula
functional organization and functional connectivity integrate
interoceptive, exteroceptive, and emotional awareness. Human
Brain Mapping, 34(11), 2944–2958. https://doi.org/10.1002
/hbm.22113, PubMed: 22696421
Sonkusare, S., Ahmedt-Aristizabal, D., Aburn, M. J., Nguyen, V. T.,
Pang, T., Frydman, S., … Guo, C. C. (2019a). Detecting changes in
facial temperature induced by a sudden auditory stimulus based on
deep learning-assisted face tracking. Scientific Reports, 9(1), 4729.
https://doi.org/10.1038/s41598-019-41172-7, PubMed: 30894584
Network Neuroscience
576
l
D
o
w
n
o
a
d
e
d
f
r
o
m
h
t
t
p
:
/
/
d
i
r
e
c
t
.
m
i
t
.
/
/
t
e
d
u
n
e
n
a
r
t
i
c
e
-
p
d
l
f
/
/
/
/
/
7
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2
1
1
8
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n
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a
_
0
0
2
9
5
p
d
t
.
f
b
y
g
u
e
s
t
t
o
n
0
7
S
e
p
e
m
b
e
r
2
0
2
3
Insula-cingulate dynamics
Sonkusare, S., Breakspear, M., & Guo, C. (2019b). Naturalistic
stimuli in neuroscience: Critically acclaimed. Trends in Cognitive
Science, 23(8), 699–714. https://doi.org/10.1016/j.tics.2019.05
.004, PubMed: 31257145
Szabadi, E. (2018). Functional organization of the sympathetic
pathways controlling the pupil: Light-inhibited and light-
stimulated pathways. Frontiers in Neurology, 9, 1069. https://
doi.org/10.3389/fneur.2018.01069, PubMed: 30619035
Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D., & Leahy, R. M.
(2011). Brainstorm: A user-friendly application for MEG/ EEG
analysis. Computational Intelligence and Neuroscience, 2011,
879716. https://doi.org/10.1155/2011/879716, PubMed:
21584256
Thomas, B. L., Claassen, N., Becker, P., & Viljoen, M. (2019). Valid-
ity of commonly used heart rate variability markers of autonomic
nervous system function. Neuropsychobiology, 78(1), 14–26.
https://doi.org/10.1159/000495519, PubMed: 30721903
Tian, Y., & Zalesky, A. (2018). Characterizing the functional con-
nectivity diversity of the insula cortex: Subregions, diversity
curves and behavior. NeuroImage, 183, 716–733. https://doi
.org/10.1016/j.neuroimage.2018.08.055, PubMed: 30172005
Tian, Y., Zalesky, A., Bousman, C., Everall, I., & Pantelis, C. (2019).
Insula functional connectivity in schizophrenia: Subregions, gra-
dients, and symptoms. Biological Psychiatry: Cognitive Neurosci-
ence and Neuroimaging, 4(4), 399–408. https://doi.org/10.1016/j
.bpsc.2018.12.003, PubMed: 30691966
Touroutoglou, A., Bliss-Moreau, E., Zhang, J., Mantini, D., Vanduffel,
W., Dickerson, B. C., & Barrett, L. F. (2016). A ventral salience net-
work in the macaque brain. NeuroImage, 132, 190–197. https://doi
.org/10.1016/j.neuroimage.2016.02.029, PubMed: 26899785
Touroutoglou, A., Hollenbeck, M., Dickerson, B. C., & Barrett, L. F.
(2012). Dissociable large-scale networks anchored in the right
anterior insula subserve affective experience and attention.
NeuroImage, 60(4), 1947–1958. https://doi.org/10.1016/j
.neuroimage.2012.02.012, PubMed: 22361166
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F.,
Etard, O., Delcroix, N., … Joliot, M. (2002). Automated anatomical
labeling of activations in SPM using a macroscopic anatomical
parcellation of the MNI MRI single-subject brain. NeuroImage,
15(1), 273–289. https://doi.org/10.1006/nimg.2001.0978,
PubMed: 11771995
Uddin, L. Q. (2015). Salience processing and insular cortical
function and dysfunction. Nature Reviews Neuroscience, 16(1),
55–61. https://doi.org/10.1038/nrn3857, PubMed: 25406711
Ullsperger, M., & von Cramon, D. Y. (2001). Subprocesses of per-
formance monitoring: A dissociation of error processing and
response competition revealed by event-related fMRI and ERPs.
NeuroImage, 14(6), 1387–1401. https://doi.org/10.1006/nimg
.2001.0935, PubMed: 11707094
Valentini, E. (2010). The role of anterior insula and anterior cingu-
late in empathy for pain. Journal of Neurophysiology, 104(2),
584–586. https://doi.org/10.1152/jn.00487.2010, PubMed:
20554847
Van de Steen, F., Almgren, H., Razi, A., Friston, K., & Marinazzo, D.
(2019). Dynamic causal modelling of fluctuating connectivity in
resting-state EEG. NeuroImage, 189, 476–484. https://doi.org/10
.1016/j.neuroimage.2019.01.055, PubMed: 30690158
van den Heuvel, M. P., Scholtens, L. H., Feldman Barrett, L.,
Hilgetag, C. C., & de Reus, M. A. (2015). Bridging cytoarchitec-
tonics and connectomics in human cerebral cortex. Journal of
Neuroscience, 35(41), 13943–13948. https://doi.org/10.1523
/JNEUROSCI.2630-15.2015, PubMed: 26468195
Von Economo, C. (2009). Cellular structure of the human cerebral
cortex. Basel, Switzerland: Karger Medical and Scientific
Publishers.
Wallentin, M., Nielsen, A. H., Vuust, P., Dohn, A., Roepstorff, A., &
Lund, T. E. (2011). Amygdala and heart rate variability responses
from listening to emotionally intense parts of a story. Neuro-
Image, 58(3), 963–973. https://doi.org/10.1016/j.neuroimage
.2011.06.077, PubMed: 21749924
Wang, X. J. (2010). Neurophysiological and computational princi-
ples of cortical rhythms in cognition. Physiological Reviews,
90(3), 1195–1268. https://doi.org/10.1152/physrev.00035.2008,
PubMed: 20664082
Weigel, J., Williams, T., & Weigel, R. (2009). The butterfly circus.
Evolution Entertainment.
Weston, C. S. (2012). Another major function of the anterior cingu-
late cortex: The representation of requirements. Neuroscience
and Biobehavioral Reviews, 36(1), 90–110. https://doi.org/10
.1016/j.neubiorev.2011.04.014, PubMed: 21554898
Xhyheri, B., Manfrini, O., Mazzolini, M., Pizzi, C., & Bugiardini, R.
(2012). Heart rate variability today. Progress in Cardiovascular
Diseases, 55(3), 321–331. https://doi.org/10.1016/j.pcad.2012
.09.001, PubMed: 23217437
Xia, M., Wang, J., & He, Y. (2013). BrainNet Viewer: A network
visualization tool for human brain connectomics. PLoS One, 8(7),
e68910. https://doi.org/10.1371/journal.pone.0068910, PubMed:
23861951
Yan, H., Zuo, X.-N., Wang, D., Wang, J., Zhu, C., Milham, M. P., …
Zang, Y. (2009). Hemispheric asymmetry in cognitive division of
anterior cingulate cortex: A resting-state functional connectivity
study. NeuroImage, 47(4), 1579–1589. https://doi.org/10.1016/j
.neuroimage.2009.05.080, PubMed: 19501172
Zalesky, A., & Breakspear, M. (2015). Towards a statistical test for
functional connectivity dynamics. NeuroImage, 114, 466–470.
https://doi.org/10.1016/j.neuroimage.2015.03.047, PubMed:
25818688
Zeidman, P., Jafarian, A., Seghier, M. L., Litvak, V., Cagnan, H.,
Price, C. J., & Friston, K. J. (2019). A guide to group effective con-
nectivity analysis, part 2: Second level analysis with PEB. Neuro-
Image, 200, 12–25. https://doi.org/10.1016/j.neuroimage.2019
.06.032, PubMed: 31226492
Network Neuroscience
577
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