For Peer Review - UNIGE€¦ · For Peer Review 3 Introduction The burden of unrelieved pain is a...

For Peer ReviewPain management decisions in emergency hospitals are

predicted by brain activity during empathy and error monitoring

Journal: British Journal of Anaesthesia

Manuscript ID BJA-2018-01024-LC070.R2

Article Type: Laboratory Investigation

Date Submitted by the Author: 29-Dec-2018

Complete List of Authors: Corradi-Dell'Acqua, Corrado; University of Geneve, Department of PsychologyFoester, Maryline; Lausanne University Hospital, Emergency DepartmentSharvit, Gil; University of Geneve, Department of Fundamental NeuroscienceTrueb, Lionel; Lausanne University Hospital, Emergency DepartmentFoucault, Eliane; Lausanne University Hospital, Emergency DepartmentFournier, Yvan; Hopital Intercantonal De La Broye Site De Payerne, Emergency DepartmentVuilleumier, Patrik; University of Geneve, Department of Fundamental NeuroscienceHugli, Olivier; Lausanne University Hospital, Emergency Department

<a href=https://www.nlm.nih.gov/mesh/MBrowser.html

target=_new>Mesh keywords</a>:Neuroimaging, Decision Making, Pain Management

British Journal of Anaesthesia

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Pain management decisions in emergency hospitals are predicted by brain

activity during empathy and error monitoring

C. Corradi-Dell’Acqua1,2*, M. Foester3, G. Sharvit2,4,5, L. Trueb3, E. Foucault3, Y. Fournier6, P.

Vuilleumier2,4,5† & O. Hugli3†

1Theory of Pain Laboratory, Department of Psychology, Faculty of Psychology and Educational

Sciences (FPSE), University of Geneva, Geneva, Switzerland.2Geneva Neuroscience Center, University of Geneva, Geneva, Switzerland.3Emergency Department, University Hospital of Lausanne (UHL), Lausanne, Switzerland.4Laboratory for Behavioural Neurology and Imaging of Cognition, Department of Neuroscience,

University of Geneva, Switzerland.5Swiss Centre for Affective Sciences, University of Geneva, Geneva, Switzerland.6Emergency Department, Hôpital intercantonal de la Broye, Payerne, Switzerland.†These authors contributed equally.

*Correspondence should be addressed to: Corrado Corradi-Dell'Acqua, University of Geneva –

Campus Biotech, Ch. des Mines 9, CH-1211, Geneva, Switzerland. Tel: +41223790958. E-mail:

[email protected]; URL: http://www.unige.ch/fapse/toplab/

Running Title: Brain signatures of pain management decisions

Manuscript Information:Running Title length: 46 characters (including spaces)Summary word count: 247 wordsManuscript word count: 2999Number of Figures: 4Number of Tables: 1Number of References: 37

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http://www.unige.ch/fapse/toplab/

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Summary

Objective. Pain undertreatment, or oligoanalgesia, is frequent in the emergency department

(ED), with major medical, ethical, and financial implications. Across different hospitals,

healthcare providers have been reported to differ considerably in the ways in which they

recognize and manage pain, with some prescribing analgesics far less frequently than others.

However, factors that could explain this variability remain poorly understood. Here, we

employed neuroscience approaches for neural signal modelling to investigate whether

individual decisions in the ED could be explained in terms of brain patterns related to empathy,

risk-taking, and error monitoring.

Methods. For fifteen months, we monitored the pain management behaviour of ED nurses at

triage, and subsequently invited them to a neuroimaging study involving three well-established

tasks probing relevant cognitive and affective dimensions. Univariate and multivariate

regressions were used to predict pain management decisions from neural activity during these

tasks.

Results. We found that the brain signal recorded when empathizing with others predicted the

frequency with which nurses documented pain in their patients. In addition, neural activity

sensitive to errors and negative outcomes predicted the frequency with which nurses denied

analgesia by registering potential side effects.

Conclusions. These results highlight the multiple processes underlying pain management, and

suggest that the neural representations of others’ states and one’s errors play a key role in

individual treatment decisions. Neuroscience models of social cognition and decision-making

are a powerful tool to explain clinical behaviour and might be used to guide future educational

programs to improve pain management in ED.

MeSH Keywords

Pain Management; Neuroimaging; Decision Making

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Introduction

The burden of unrelieved pain is a major unresolved public health problem, resulting in human

suffering and economic costs. Unlike other medical conditions, pain is difficult to quantify

objectively, and is mainly assessed using self-reports and indirect information about its intensity

and aetiology, including medical history, previous experience, etc. As such, pain is frequently

undertreated in hospitals (oligoanalgesia)1,2, an issue which is exacerbated by the fact that

healthcare providers vary widely in the willingness to prescribe analgesics, with only a fraction

of this variability explainable by simple demographic characteristics (gender, age or

professional experience)3–7.

In the last years, Emergency Departments (ED) worldwide have introduced

computerized protocols to guide nurses at diagnosing and managing pain. Although these

approaches improved the overall quality of pain management8–10, they did not counteract

oligoanalgesia, as ED nurses still underestimated and undertreated patients’ pain to a variable

degree11–14. This begs for the introduction of new approaches to better understand the

processes underlying individual pain management decisions, which could lead to appropriate

training procedures to reduce practice variation.

In the present study we exploited recent advances in cognitive and affective

neuroscience, which identified brain patterns related to personal affect and decision-making. In

particular, a network involving the insula, cingulate cortex, and postcentral gyrus, was

consistently implicated in empathizing with other people’s pain15,16. In addition, a partially-

overlapping network in the anterior cingulate, anterior insula, and lateral prefrontal cortex was

systematically associated with monitoring errors and negative outcomes from one’s


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choices17,18. This growing knowledge about brain functions provided an opportunity to

understand the processes underlying individual differences in pain management. In particular,

we hypothesized that brain patterns related to empathy might explain individual differences in

diagnosis, as healthcare providers who are less sensitive to others’ suffering might report less

the pain of their patients. Further, we predicted that brain patterns related to error-processing

might also influence decisions at the bedside, as individuals most concerned about their

performance might refrain from administering analgesics in fear their side effects.

Methods

Ethics Approval

The study was approved by the Ethical Commission of Canton Vaud (CER-VD N°95/13) and

conducted according to the declaration of Helsinki. Each participant signed an informed

consent form.

Nurse-Initiated Analgesia Protocol

This study took advantage of a nurse-initiated analgesia protocol implemented in 2013 in the

ED of the Lausanne University Hospital (Switzerland). The ED receives around 40,000 patients

annually, each of which is initially triaged through the Swiss Emergency Triage Scale19. Each

nurse certified at using the protocol was prompted by an electronic health record (EHR) to

report: (a) whether the patient was in pain (> 0 using a numeric rating scale ranging from 0 [no

pain] to 10 [the worst pain imaginable]); (b) whether there were contraindications to analgesia;

(c) whether the patient wished to receive analgesia; (d) whether an appropriate treatment

(paracetamol, ibuprofen, tramadol) should be selected (Figure 1A). Importantly, as protocol


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data were recorded at triage, the assignment of patients to nurses was based exclusively on

personnel availability, without any preselection in terms of acuity/aetiology. Hence, the nurses’

identity was independent from the cases examined.

Pain Management Measures

We used the EHR to retrieve information about the pain management decisions of each

certified nurse for 15 months following the protocol implementation. Specifically, we focused

on data from eligible patients (> 16 years old, in pain for less than 3 months, without history of

drug/alcohol abuse, and no life-threatening condition) to estimate the following measures (see

Figure 1A for more details):

1. Treatment Application: proportion of decisions to deliver analgesia on triaged patients.

This index was then broken down into two sub-indexes:

2. Documentation Rate: the proportion of pain documentations on triaged patients.

3. Contraindication (CI) Rate: the proportion of CIs to analgesia documented in those

patients who were in pain.

Participants

Nine months after the protocol implementation, all certified nurses were invited to take part to

a survey probing for demographic information, work experience, and the anxiety from

uncertainty scale20. Subsequently, between 16-18 months after the protocol implementation, a

subgroup was invited to take part to a study involving functional Magnetic Resonance Imaging

(fMRI). This subgroup comprehended equal proportion of individuals from each tertile of the

Treatment Application distribution obtained from a preliminary analysis of protocol data (6


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months from the implementation). This selection ensured that the tested individuals would

represent a broad spectrum of protocol use.

Neuroimaging Intervention

The neuroimaging study involved the following three experimental paradigms (see

Supplements for more details).

1. Empathy for pain task15,21. Nurses saw pictures depicting hands in painful situations

(wounded, pierced by a syringe, etc.), and control stimuli involving hands without any

aversive feature. The task included 30 stimuli per condition, each presented for 2.5 sec

and followed by an inter-stimulus interval ranging between 2.5-4.1 sec. This task lasted

about 15 minutes.

2. Balloon Analog Risk Task (BART)22,23. Nurses had to adjust to risk in a gambling context,

by pressing a key repeatedly to inflate a virtual balloon as much as possible and stop just

before it exploded. If they stopped before the explosion, they received a virtual

monetary gain proportional to the volume of air pumped (win condition); however, they

received nothing if the balloon exploded (loss condition). The task involved 28 game

iterations, each leading to a potential win/loss. Every game comprehended up to 11

inflations, each remaining on the screen until a response was provided, and followed by

an inter-inflation interval ranging between 1.5-2.5 sec. Win/loss feedbacks lasted 2.5 sec

and were followed by an interval ranging between 2-4 sec. The task never exceeded 15

minutes.

3. Social Harm Avoidance Monitoring Experiment (SHAME)24. We implemented an error-

monitoring task involving similar stakes to clinical decision making, where one’s errors


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may cause harm to another person (the patient). The nurse inside the scanner took

turns with a colleague outside (another nurse from the experimental group) in

performing a dot-counting task. Overall, there were 98 trials, organized in 14 blocks (7

per player) of 7 trials each. Every erroneous response had a 50% probability to cause a

painful stimulation to the arm of the nurse outside the scanner, and was signalled with

an ad hoc feedback for 5 sec, followed by an interval ranging between 2-9 sec. The

overall amount of correct/erroneous trials depended on participants’ proficiency in the

counting task, whose difficulty was adjusted on-line to avoid ceiling/floor effects. The

critical condition was when the nurse in the scanner caused pain to the one outside

(one’s painful errors). This was compared with a condition in which the same harmful

outcome was caused by the nurse outside to him/herself (others’ painful errors). The

task lasted 12 minutes.

Data Analysis

In the behavioural survey, we first assessed the dependency between the three pain

management measures through Pearson’s correlation coefficient. Subsequently, we assessed

how each of these three measures was related with age, gender, years of experience and

anxiety for uncertainty. Results are reported as significant under an α = 0.003 (Bonferroni-

corrected for 15 tests). Uncorrected effects (α = 0.05) associated with anxiety for uncertainty

scores are also reported, as one of the aims of the study was to investigate specifically how

error/uncertainty processing might affect different stages of pain management.

For the neuroimaging investigation, we first preprocessed functional data of each nurse

using SPM12 software (http://www.fil.ion.ucl.ac.uk/spm/) to account for head movements,


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geometric distortions by the magnetic field, and anatomical differences between subjects. The

preprocessed images were then fed to first-level General Linear Models (GLMs) testing, in each

task, for increased activity in the main condition of interest, and for the tailored control (see

previous studies15,21–24 and supplements for details). The activity maps estimated in each

individual GLM were then used for group-level analyses testing whether the condition of

interest in each task: (a) exhibited increased activity with respect to the control; (b) was linearly

modulated by nurses’ professional behaviour. Activations were reported if surviving correction

for multiple comparisons for the whole brain or for regions-of-interest masks. These masks

were obtained by reanalysing, under the same parameters used here, previous datasets

obtained by running the same three paradigms on lay individuals15,23,24 (see Supplements and

Tables S1-3 for more details).

In addition, we used Least Absolute Shrinkage and Selection Operator (LASSO)25–28 and

Random Forest (RF) regression29 to identify distributed patterns of activity that could predict

nurses’ professional behaviour. In particular, this analysis involved: (1) extracting the activity

associated with each event of interest from a priori masks (the same used for the univariate

analysis). (2) Feeding the extracted signal to the two algorithms for multivariate modelling. (3)

Testing the generalizability of the estimated models through cross-validation techniques: i.e.,

assessing whether a model tailored on a portion of subjects could predict the clinical behaviour

of the remaining (independent) subjects. (4) Obtaining an overall mean squared error (MSE) as

measure of prediction proficiency, which was then validated statistically through permutation

techniques (see Supplements).


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Results

70 ED nurses responded to the survey, 33 of which agreed to take part to a subsequent

neuroimaging investigation (see Table 1 for details). Two nurses asked to discontinue the

neuroimaging session prematurely: hence, BART was completed by 32 participants, and SHAME

by 31.

Behavioural survey

When assessing the nurse-led analgesia protocol data, we found a large inter-individual

variability in Treatment Application (Figure 1B). This variability was related to both individual

Documentation Rate and CI rate: nurses that applied analgesia more frequently were more

inclined to document patients’ pain (r = 0.36, p = 0.002), and less likely to report

contraindications (r = -0.54, p < 0.001) (Figure 1C). None of these indexes were associated with

nurses’ age, years of experience (│r│ ≤ 0.17, n.s.) or gender (│t│ ≤ 0.99; except for potentially

larger Documentation Rate in males nurses t(30.31) = 2.15, p = 0.039, uncorrected). Interestingly,

nurses with higher scores on the anxiety from uncertainty scale showed higher CI rates (r =

0.29, p = 0.017 uncorrected; for the other indexes |r| ≤ 0.18, n.s.).

Neural responses to Others’ Pain

Subsequently, we engaged a subgroup of nurses in a fMRI task where they witnessed pictures

of injured hands. This task recruited a brain network classically associated with pain-processing

and empathy15,16,21, involving the posterior insula, postcentral gyrus, and midline cortical areas

(Figure 2A). No activation was observed in the anterior insula and middle cingulate cortex,

which are known to respond to others’ pain in lay individuals, but not in professional healthcare

providers30,31.


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We then tested whether these neural responses to others’ pain could predict nurses’

clinical behaviour. First, by using a univariate linear regression, we found a significant

relationship between the activity the right postcentral cortex and Documentation Rate, with

stronger neural response to injured hands in those who reported most frequently patients’ pain

in their daily work. We then tested whether clinical behaviour could be predicted from

distributed patterns of brain activity (rather than isolated regions) during this task. For this

purpose, we extracted the neural activity evoked by viewing injured hands from a predefined

network (see Methods), and fed it to two machine learning algorithms (LASSO and RF) to

predict clinical behaviour. Both algorithms revealed that empathy-related activity was a good

predictor of the documentation rate of individual nurses (Figure 2B). No significant effects

(neither univariate nor multivariate) were associated with the other two measures.

Neural responses to Negative Outcomes

We performed similar analyses for brain activity evoked when observing self-caused errors and

negative outcomes. When confronted with monetary losses (vs. gains) in the BART22,23, nurses

exhibited widespread activations in the middle cingulate cortex, anterior insula, and thalamus

(Figure 3A), a network often associated with the detection of errors17,18, and other salient

outcomes32,33. Univariate linear regression showed that the activity of several regions within

this network, including the insula and cingulate areas, were related to the documentation of

contraindications to analgesia. In addition, multivariate regression with LASSO and RF revealed

that distributed patterns of activity related to money loss was a reliable predictor of nurses’ CI

rate (Figure 3B).


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Similarly, when observing harmful consequences of their own (vs. someone else) errors

in the SHAME24, nurses activated the anterior portion of the middle cingulate cortex. Moreover,

regression analysis showed that activity related to one’s painful errors was linearly coupled

with CI rate in both the middle cingulate cortex and left middle frontal gyrus. Thus, as found for

the BART, these areas were more strongly activated in those individuals who were more likely

to spot contraindications to analgesia. Finally, LASSO and RF regression confirmed that activity

patterns in the network activated by harmful errors were a reliable predictor of CI Rate (Figure

4). Data from neither BART nor SHAME were significantly associated with the other two clinical

measures.

Discussion

Healthcare providers appraise and treat pain very differently from one another3–7, resulting in

patients being more or less likely to receive analgesia according to the person who is in charge

of them. The demographic characteristics of healthcare providers explain only partially this

variability3, suggesting that other factors are at play. By using a battery of well-established

questionnaires20 and experimental paradigms from neuroscience15,21–24, we shed new light on

the mechanisms underlying these inter-individual differences. First, the likelihood of reporting

contraindications to analgesia in clinical practice can be explained by personal anxiety towards

uncertain outcomes (from the behavioural survey), as well as differences in brain responses to

negative feedbacks (neuroimaging investigation). Second, the frequency of documenting

patients’ pain can be explained by differences in brain patterns evoked by witnessing others’

injuries. Overall, our study underscores the role played by two main processes which exert


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opposite, but concurrent influences on the decision leading to the prescription of analgesia in

clinical practice.

Ideally, choices such as documenting a symptom, reporting contraindications or

prescribing treatment should be motivated exclusively by the clinical characteristics of patients.

Hence, no variability should be observed between ED nurses, as long as they all handle a similar

mix of cases, matched in aetiology and severity. Surprisingly however, nurses behave quite

differently from one another, ranging from those who prescribe analgesia to ~5% up to 20% of

patients (Figure 1B; see also3–7). Considering that patients’ assignment was independent of the

nurses’ identity, and that the clinical variables of interest were obtained by collapsing data from

all cases handled by each operator in 15 months (see Methods), it is unlikely that the observed

variability was influenced by the severity of patients examined. Instead, it is more plausible that

each nurse is characterized by a personal disposition/attitude towards pain management.

Previous studies have already categorized healthcare providers according to their attitudes

(more vs. less attentive to case severity5, more vs. less reliant on patients’ self-reports11),

without however shedding light on the processes that might contribute to this categorization.

Our study extends previous findings, not only by providing a working model according to which

pain management is driven by two clear dimensions, but also by associating these processes

with distinct brain networks.

Brain responses evoked by observing others’ pain have been thoroughly investigated in

neuroscience research, pointing to a major role of the insula, middle cingulate cortex, and

postcentral gyrus16. The most popular interpretation of these activations is that they reflect the

engagement of circuits implicated in first-hand nociception, which are then re-enacted


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“empathetically” when pain is not felt on oneself but observed in others15,16. Critically,

however, these regions are not homogeneous in their function, but can be broadly classified

into two functionally-segregated networks, coding different aspects of the painful experience.

In particular, brain patterns in the anterior insula and middle cingulate cortex might not be

pain-specific, but generalize also to other aversive experiences such as arousing pictures15,

disgusting tastes, or monetary losses34. Hence, these regions could serve a domain-general

purpose involved in detecting events of high relevance for one’s survival32, including errors17,18

and risky decisions22,23, with painful or financial consequences for oneself and others33. In

contrast, the posterior insula and postcentral somatosensory cortex appear to process pain in a

more specific fashion, with little generalization to other forms of affect15,35. This might underlie

a sensory-specific component of the painful experience, which is re-enacted when witnessing

also others’ sufferance15,16. In our study, these functionally segregated networks were

associated with independent components of pain management, with the postcentral gyrus

predicting the frequency with which healthcare providers documented pain in patients, and the

middle cingulate cortex predicting the frequency with which they noted potential

contraindications.

Overall, our study offers a comprehensive model of pain management decisions in

which healthcare providers hold at least two distinct representations of their patient’s state.

First, there is the patient’s current pain, which is estimated through evaluation of diagnostic

signs as well as self-reports, but also influenced by doctors and nurses’ empathic skills. Second,

there is the patient’s prospective state, which is estimated by predicting the potential

consequences of analgesia and thus taps into one’s ability to make decisions under uncertainty


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and to learn from previous errors. Critically, although healthcare providers are deontologically

bound to relieve patients’ current pain with analgesia, they are equally bound to prevent

potential side-effects by withholding analgesia, a conflict which is resolved differently in each

individual, based on specific characteristics of the case, but also personal traits of empathy,

dispositions towards errors/uncertainty, etc. Training techniques already exist to modulate

empathy and compassion36, but also to help individuals reduce anxiety about potential errors37.

These could serve as a basis for future educational programs for doctors and nurses, to

promote a more efficient pain treatment and a more coherent level of care.

In this study, we exploited the rare opportunity to monitor pain management

behaviours of professional healthcare providers for 15 months, and relate them to brain activity

patterns in well-known tasks. The drawback of this approach lies in the difficulty of obtaining

independent cohorts (e.g., for assessing power or replicating effects), as other hospitals usually

do not record the same behavioural indexes. The application of rigorous cross-validation

techniques insured generalizability within the sample tested. However only future

implementations of the same pain management protocol in other EDs will allow extend our

findings to different countries and healthcare systems.

Funding

This study was supported by the Swiss National Science Foundation grant n. PP00O1_157424/1

(C.C.D.). The salary of one author (M.F.) was partially supported by an unrestricted grant from

the UPSA pain foundation and the French Society for Emergency Medicine (SFMU).


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Authors’ contribution

C.C.D.: Study design, Data collection, Data analysis, Interpretation of Results, Manuscript drafting.M.F.: Study design, Subjects recruitment, Manuscript critical revision.G.S.: Data collection, Manuscript critical revision.L.T.: Data Analysis, Manuscript critical revision.E.F.: Subjects recruitment, Manuscript critical revision.Y.F.: Study design, Manuscript critical revision.P.V.: Study design, Interpretation of Results, Manuscript critical revision.O.H.: Study design, Interpretation of Results, Manuscript critical revision.

All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work (thereby ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved).

Declaration of Interest

None Declared.

Acknowledgments

We would like to thank Matthias Zunhammer for his advices in relation to the LASSO

method and Franca Davenport and Kimberly C. Doell for overseeing the quality of the English

text.

Appendix

De-identified data files and scripts for the multivariate analyses are available at Open

Science Framework: https://osf.io/2bved/.


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https://osf.io/2bved/

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24. Koban L, Corradi-Dell’Acqua C, Vuilleumier P. Integration of error agency and representation of others’ pain in the anterior insula. J Cogn Neurosci 2013; 25: 258–72

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27. Krishnan A, Woo C-W, Chang LJ, et al. Somatic and vicarious pain are represented by dissociable multivariate brain patterns. eLife 2016; 5: e15166


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28. Zunhammer M, Geis S, Busch V, Eichhammer P, Greenlee MW. Pain modulation by intranasal oxytocin and emotional picture viewing — a randomized double-blind fMRI study. Scientific Reports 2016; 6: 31606

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31. Cheng Y, Chen C, Decety J. How Situational Context Impacts Empathic Responses and Brain Activation Patterns. Front Behav Neurosci 2017; 11

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Figure Legends

Figure 1. (A) Flowchart subsuming the key steps of the nurse-led protocol implemented in the

Emergency Department. Nurses were expected to follow and document this procedure for each

patient under their care. Data collected for each nurse over 15 months following the protocol

implementation were used to estimate three different scalars indexing their pain management

behaviour (Pain Documentation Rate, CI Rate, and Treatment Application). Each measure was

computed as the percentage among patients who passed a specific protocol step, as noted in

the flowchart. Full details in methods section. (B) Bar-graphs displaying between-nurse

variability in pain management behaviour. Each subplot represents one of the three scalars of

interest, whereas each bar represents one isolated nurse. Nurses’ identity is here coded with a

number ranging from 1 to 70 according to their percentage of Treatment Application value. (C)

Scatter plots describing the linear relation between the three measures. (D) Scatter plots

describing the linear relation between the Anxiety due to Uncertainty score and each of the

three behavioural measures of interest. Each plot shows a linear regression line (with a grey

area describing the 95% confidence interval), plus the Pearson correlation coefficient. The

significance of the correlation is highlighted as follows: ***p < 0.001, **p < 0.01, *p < 0.05.

Figure 2. Empathy for Pain (A) Whole brain map depicting regions implicated in processing

pictures of injured hands (Painful – Control Images). (B) Linear regression of Documentation

Rate. Surface rendering of a human brain highlighting suprathreshold coordinates in which

neural responses to Painful Images explained nurses’ Documentation rate in univariate linear

regression. Three subplots are also displayed. The left-low subplot describes the linear relation


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20

between Documentation Rate and the average parameter extracted by the right Postcentral

Gyrus (grey area refers to the 95% confidence interval). The remaining two subplots refer to

data from Multivariate Pattern Analysis (color-coded according to the machine-learning

algorithm used). On top, the overall proficiency of LASSO and RF classifiers for prediction of the

three clinical measures of interest is displayed. White circles refer to mean square error (MSE)

associated with out-of-subject predictions, superimposed with violin-plots of the permutation-

based null distribution of MSE. The right-low subplot describes the linear regression between

nurses’ Documentation rate and the value predicted by each of the two classifiers. PostC:

Postcentral Gyrus. PreC: Precentral Gyrus. SMG: Supramarginal Gyrus. IFG: Inferior Frontal

Gyrus. Ins: Insula. r: Pearson correlation coefficient. ***p < 0.001, *p < 0.05 associated with

standard parametric analysis (for linear regressions) and permutation-based analysis (for

MVPA).

Figure 3. BART (A) Whole brain map depicting regions implicated in Money Loss (Loss – Win).

(B) Linear regression of CI Rate. Surface rendering of a human brain highlighting suprathreshold

coordinates in which neural responses to Money Loss explained nurses’ CI rate in univariate

linear regression. Three subplots are also displayed. The left-low subplot describes the linear

relation between CI Rate and the average parameter extracted by the Middle Cingulate Cortex

(grey area refers to the 95% confidence interval). The remaining two subplots refer to data

from Multivariate Pattern Analysis (color-coded according to the machine-learning algorithm

used). On top, the overall proficiency of LASSO and RF classifiers for prediction of the three

clinical measures of interest. White circles refer to mean square error (MSE) associated with

out-of-subject predictions, superimposed with violin-plots of the permutation-based null


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21

distribution of MSE. The right-low subplot describes the linear regression between nurses’ CI

rate and the value predicted by each of the two classifiers. MCC: Middle Cingulate Cortex. PreC:

Precentral Gyrus. Ins: Insula. OP: Parietal Operculum. r: Pearson correlation coefficient. ***p <

0.01, **p < 0.01, *p < 0.05 associated with standard parametric analysis (for linear regressions)

and permutation-based analysis (for MVPA).

Figure 4. SHAME (A) Whole brain map depicting regions implicated in painful outcomes of one’s

errors (One’s – Others’ Painful Errors). (B) Linear regression of CI Rate. Surface rendering of a

human brain highlighting suprathreshold coordinates in which neural responses to One’s

Painful errors explained nurses’ CI rate in univariate linear regression. Three subplots are also

displayed. The left-low subplot describes the linear relation between CI Rate and the average

parameter extracted by the anterior Middle Cingulate Cortex (grey area refers to the 95%

confidence interval). The remaining two subplots refer to data from Multivariate Pattern

Analysis (color-coded according to the machine-learning algorithm used). On the top the overall

proficiency of LASSO and RF classifiers for prediction of the three clinical measures of interest.

White circles refer to mean square error (MSE) associated with out-of-subject predictions,

superimposed with violin-plots of the permutation-based null distribution of MSE. The right-low

subplot describes the linear regression between nurses’ CI rate and the value predicted by each

of the two classifiers. aMCC: anterior Middle Cingulate Cortex. MFG: Middle Frontal Gyrus.

***p < 0.001, *p < 0.05 associated with standard parametric analysis (for linear regressions)

and permutation-based analysis (for MVPA).


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Tables

Table 1

Demographic information. Eligible ED nurses responding to the survey, and subsequently

subdivided into those who took part to the neuroimaging investigation, and those who did not.

Each of the three groups is described in terms of overall size, number of women (including

percentage value to the overall size), and median age, experience in ED and number of triages

per nurse in a time window of 15 months (bracket values refer to inter-quartile range). For each

of measures reported, the subgroup taking part to the neuroimaging investigation discloses

similar values to the group who did not.

SurveyNeuroimaging

Participants

Other

Participants

Population Size 70 33 37

Females 51 (73%) 22 (67%) 29 (78%)

Age [years] 33 [31, 38] 34 [31, 39] 33 [30, 37]

ED Experience [years]

6 [4, 9] 9 [4, 13] 6 [4, 8]

Triages per nurse 452 [273, 694] 480 [405, 694] 445 [210, 692]


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(A) Flowchart subsuming the key steps of the nurse-led protocol implemented in the Emergency Department. Nurses were expected to follow and document this procedure for each patient under their care. Data collected for each nurse over 15 months following the protocol implementation were used to estimate three different scalars indexing their pain management behaviour (Pain Documentation Rate, CI Rate, and

Treatment Application). Each measure was computed as the percentage among patients who passed a specific protocol step, as noted in the flowchart. Full details in methods section. (B) Bar-graphs displaying between-nurse variability in pain management behaviour. Each subplot represents one of the three scalars of interest, whereas each bar represents one isolated nurse. Nurses’ identity is here coded with a number

ranging from 1 to 70 according to their percentage of Treatment Application value. (C) Scatter plots describing the linear relation between the three measures. (D) Scatter plots describing the linear relation

between the Anxiety due to Uncertainty score and each of the three behavioural measures of interest. Each plot shows a linear regression line (with a grey area describing the 95% confidence interval), plus the

Pearson correlation coefficient. The significance of the correlation is highlighted as follows: ***p < 0.001, **p < 0.01, *p < 0.05.


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Empathy for Pain (A) Whole brain map depicting regions implicated in processing pictures of injured hands (Painful – Control Images). (B) Linear regression of Documentation Rate. Surface rendering of a human

brain highlighting suprathreshold coordinates in which neural responses to Painful Images explained nurses’ Documentation rate in univariate linear regression. Three subplots are also displayed. The left-low subplot

describes the linear relation between Documentation Rate and the average parameter extracted by the right Postcentral Gyrus (grey area refers to the 95% confidence interval). The remaining two subplots refer to

data from Multivariate Pattern Analysis (color-coded according to the machine-learning algorithm used). On top, the overall proficiency of LASSO and RF classifiers for prediction of the three clinical measures of interest is displayed. White circles refer to mean square error (MSE) associated with out-of-subject

predictions, superimposed with violin-plots of the permutation-based null distribution of MSE. The right-low subplot describes the linear regression between nurses’ Documentation rate and the value predicted by each

of the two classifiers. PostC: Postcentral Gyrus. PreC: Precentral Gyrus. SMG: Supramarginal Gyrus. IFG: Inferior Frontal Gyrus. Ins: Insula. r: Pearson correlation coefficient. ***p < 0.001, *p < 0.05 associated

with standard parametric analysis (for linear regressions) and permutation-based analysis (for MVPA).


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BART (A) Whole brain map depicting regions implicated in Money Loss (Loss – Win). (B) Linear regression of CI Rate. Surface rendering of a human brain highlighting suprathreshold coordinates in which neural

responses to Money Loss explained nurses’ CI rate in univariate linear regression. Three subplots are also displayed. The left-low subplot describes the linear relation between CI Rate and the average parameter

extracted by the Middle Cingulate Cortex (grey area refers to the 95% confidence interval). The remaining two subplots refer to data from Multivariate Pattern Analysis (color-coded according to the machine-learning

algorithm used). On top, the overall proficiency of LASSO and RF classifiers for prediction of the three clinical measures of interest. White circles refer to mean square error (MSE) associated with out-of-subject predictions, superimposed with violin-plots of the permutation-based null distribution of MSE. The right-low subplot describes the linear regression between nurses’ CI rate and the value predicted by each of the two classifiers. MCC: Middle Cingulate Cortex. PreC: Precentral Gyrus. Ins: Insula. OP: Parietal Operculum. r: Pearson correlation coefficient. ***p < 0.01, **p < 0.01, *p < 0.05 associated with standard parametric

analysis (for linear regressions) and permutation-based analysis (for MVPA).


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Figure 4. SHAME (A) Whole brain map depicting regions implicated in painful outcomes of one’s errors (One’s – Others’ Painful Errors). (B) Linear regression of CI Rate. Surface rendering of a human brain

highlighting suprathreshold coordinates in which neural responses to One’s Painful errors explained nurses’ CI rate in univariate linear regression. Three subplots are also displayed. The left-low subplot describes the

linear relation between CI Rate and the average parameter extracted by the anterior Middle Cingulate Cortex (grey area refers to the 95% confidence interval). The remaining two subplots refer to data from

Multivariate Pattern Analysis (color-coded according to the machine-learning algorithm used). On the top the overall proficiency of LASSO and RF classifiers for prediction of the three clinical measures of interest. White

circles refer to mean square error (MSE) associated with out-of-subject predictions, superimposed with violin-plots of the permutation-based null distribution of MSE. The right-low subplot describes the linear

regression between nurses’ CI rate and the value predicted by each of the two classifiers. aMCC: anterior Middle Cingulate Cortex. MFG: Middle Frontal Gyrus. ***p < 0.001, *p < 0.05 associated with standard

parametric analysis (for linear regressions) and permutation-based analysis (for MVPA).


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1

Supplementary Information

for the manuscript “Pain management decisions in emergency hospitals are predicted by brain

activity during empathy and error monitoring” by C. Corradi-Dell’Acqua, M. Foester, G. Sharvit,

L. Trueb, E. Foucault, Y. Fournier, P. Vuilleumier & O. Hugli

2

Supplementary Methods

Detailed description of the neuroimaging tasks

ED nurses were engaged in a neuroimaging study involving three experimental paradigms

testing empathy, risk taking, and error monitoring. The experiment took place in the Brain and

Behaviour Laboratory (http://bbl.unige.ch/) of the University of Geneva. All participants met

MRI safety requirements (no metallic objects in the body, no familial history of epilepsy, etc.),

and were placed supine in the scanner with the head fixated by firm foam pads. Stimuli were

presented on an LCD projector using either E-Prime 2.0 (Psychology Software Tools, Inc.) or

Cogent 2000 (Wellcome Dept., London, UK), and were observed through a mirror mounted on

the MRI headcoil. Key-presses were recorded on an MRI-compatible bimanual response button

box. The paradigms employed were the following.

Empathy for pain. In this task, 120 colour pictures were presented depicting hands in

either painful or non-painful situations. These pictures were sorted in four categories of 30

images each. Painful images described hands in pain, as visible by wounds/marks on the skin

and by the display of an object (scalpel, syringe, etc.) acting on the skin surface. Control images

were neutral stimuli matched with the previous category for hand laterality (right/left),

orientation, and associated visual content (presence of objects), but purged from any

painful/arousing feature. Arousing (and ArousingControl) images described hands in

emotionally aversive (and matched neutral controls), but painless situations (hands holding

knifes/guns, hands with handcuffs). Each of these 120 stimuli was presented for 2500 ms,

followed by an inter-trial interval that ranged from 2500 to 4100 ms (mean = 3300 ms) with

http://bbl.unige.ch/

3

incremental steps of 320 ms. Participants were asked to perform a handedness task, i.e., press

one key if the stimulus depicted a right hand but another key if the stimulus was a left hand.

The 120 stimuli were presented in a randomized order together with 30 null-events, in which an

empty screen replaced the stimuli. This task was built using E-Prime 2.0 (Psychology Software

Tools, Inc.) and lasted about 15 minutes.

Balloon Analogue Risk Task (BART). Nurses had to press a key repeatedly in order to

inflate a virtual balloon as much as possible, and stop just before it exploded. If they stopped

before explosion, they received a monetary gain proportional to the volume of air pumped;

however they got nothing if the balloon exploded. Each trial started with an empty balloon

placed on a tip of an inflator. The balloon could then be inflated for a maximum of 11 times

through key-press, each of which was associated with an increasing probability of explosion

(from 0% to 100%), but also an increased monetary reward (from 0.1 to 4.3 CHF). In this

context, participants’ choice to inflate the balloon led to two possible feedbacks: a larger

balloon together with the graphical display of the current monetary gain (e.g., “+ 0.6 CHF”), or a

“negative” feedback in which a picture of the balloon explosion was displayed together with the

text “you have lost”. At each step, participants were free to discontinue the inflation, which led

to a “positive” feedback (“you have won”), and the money amount gained during this trial was

added to their overall earning.

The experimental session comprised 28 independent trials, each separated by an inter-

trial interval ranging between 2000 and 4000 ms. Within each trial, different inflations were

separated by an interval ranging between 1500 and 2500 ms. During that time, the inflator was

coloured in red, to signal participants to withhold any response until it got green. Win/loss

4

feedbacks lasted 2000 ms. Once every six trials, participants were displayed the question “to

which extent this task makes you feel anxious?” together with a visual analog scale that ranged

from “not anxious at all” to “extremely anxious”. Participants had 5 seconds to slide a marker

on the bar until it reached the position corresponding to their judgment. The overall

experimental session never exceeded 15 minutes. This task was built using Cogent 2000

(Wellcome Dept., London, UK).

Social Harm Avoidance Monitoring Experiment (SHAME). The nurse inside the MRI

scanner took turns with a colleague outside the scanner in performing and observing a dot-

counting task. The colleague was one of the other nurses of the experimental group, who was

matched with the participant based on availability (with no further constraint in terms of

seniority or interpersonal closeness). The experiment was organized in 14 blocks, 7 in which the

subject in the scanner performed the task (whilst the subject outside the scanner observed the

same display), alternating with 7 in which the task was performed by the subject outside. Each

block comprised 5 trials, each starting with the simultaneous presentation of two clusters of

white dots on a grey background separated by a vertical line (duration 500 ms, with inter-

stimulus interval ranging from 1.5–3.5 s). The participants in charge of playing indicated which

side contained the largest amount of dots. The trial difficulty was adjusted on-line throughout

the whole experiment (at participant unawareness) to ensure a comparable amount of correct

and incorrect trials for each participant1.

Critically, each response was followed by a thermal stimulation given to the arm of the

participant outside the scanner (3 s of raise time, 2 s of plateau, 3 s for returning to baseline).

Correct responses were always followed by a painless temperature (38°C), whereas incorrect

5

responses had 50% of probability to be associated with a painless or painful temperature (set

by subject-specific pain threshold; average 48°C ± 1.45; see below for details). The thermal

stimulus was associated with a visual feedback (5 s) informing about the performance in the

task and the painfulness of the temperature, and was followed by inter-trial-interval of variable

duration (from 2-9s). The thermal stimulation was delivered through a MSA Thermotest

thermode (SOMEDIC Sales AB, Sweden). This task was built using Cogent 2000 and lasted about

12 minutes.

Pain Thresholding Procedure

In line with previous studies, individual temperatures were determined through a double

random staircase (DRS) algorithm2 3. Our DRS procedure selected a given temperature on each

experimental trial according to the previous response of the participant in a pain

unpleasantness rating scale. Trials rated as more unpleasant than the given cut-off

(corresponding to 8 out of 10 on a visual analogic scale) led to a subsequent lower temperature

in the next trial; whereas trials rated as less unpleasant than the given cut-off led to a

subsequent higher temperature. This resulted in a sequence of temperatures that rapidly

ascended towards, and subsequently converged around, a subjective pain unpleasantness

threshold, which was in turn calculated as the average value of the first four temperatures

leading to a direction change in the sequence. In order to avoid participants anticipating a

systematic relationship between their rating and the subsequent temperature, two

independent staircases were presented randomly. Initial thermal stimulations for the two

staircases were 41°C and 43°C. Within each staircase, stimulus temperatures increased or

6

decreased with steps of 3°C, while smaller changes (1°C) occurred following direction flips in

the sequence. None of our subjects was stimulated at temperature larger than 52°C.

Post-Scanning quantitative debrief session

The nurses taking part to the neuroimaging study were subjected to a post-scanning debriefing

session of about 50 minutes, which comprehended standardized tests as well as a set of

custom-based items. In particular, participants underwent the inclusion of other in the self (IOS)

scale4 to assess to which extent the felt close with the “colleague” engaged in the SHAME

outside the MRI scanner. Furthermore, participants also rated the degree to which they felt

particular emotions (pain, fear, shame, guilt, sadness, and anger) when they were engaged in

the SHAME in the MRI scanner (but not when they were performing the task as confederates

outside the scanner). All ratings were carried out on a Liker scale ranging from 1 to 5, with the

exception of the rating of pain which was carried out on a verbal numeric rating scale ranging

from 1 to 10. Participants were asked to rate their subjective experience associated with any

kind of error event: i.e., they did a mistake or when they observed their colleague making a

mistake, either with a painful or painless outcome. See Koban et al.1 for more details about this

rating session.

Finally, participants were asked to rate each of the 120 stimuli used in the Empathy for

Pain paradigms in terms of familiarity (“how much is the content described in this picture

familiar to you?”), emotional intensity (“how intense is the emotion triggered by this image?”),

emotional valence (“does this image elicit positive or negative emotions?”), and pain (“how

intense is the pain felt by the hand depicted on this image?”). The rating session was carried

7

using E-Prime 2.0, and divided in four blocks, one for each question, during which all 120 stimuli

were rated on a Likert scale rating from 1 to 10 (with the exception of the emotional valence

rating, in which a Likert scale ranging from -4 to +4 was used). To avoid habituation biases due

to the presentation of the same stimuli four times, the order of the blocks and order of the

stimuli within each block was randomized across participants. See Corradi-Dell’Acqua et al.5 for

more details about the subjective rating session.

Imaging processing

Data Acquisition. Functional images were acquired using a 3T whole-body scanner (Trio

TIM, Siemens) with a 32-channel head coil. We used a multiplex sequence6, with TR = 650 ms,

TE = 30 ms, flip angle = 50°, 36 interleaved slices, 64 x 64 in-slice resolution, 3 x 3 x 3 mm voxel

size, and 3.9 mm slice spacing. The multiband accelerator factor was 4, and parallel acquisition

techniques (PAT) was not used. A fieldmap was also estimated through the acquisition of 2

functional images with a different echo times (short TE = 5.19 ms; long TE = 7.65). Finally, a

structural image was acquired using a T1 weighted 3D sequence (MPRAGE, TR = 1900 ms, TI =

900 ms, TE = 2.27 ms, flip angle = 9°, PAT factor = 2, 192 sagittal slices, 1 x 1 x 1 mm voxel size,

256 x 256 in-slice resolution.

Preprocessing. Statistical analysis was performed using the SPM12 software

(http://www.fil.ion.ucl.ac.uk/spm/). For each subject, all functional images were realigned and

unwrapped using a field map image, to account for geometric distortions due to magnetic field

inhomogeneity. Subsequently the functional images were normalized to a template based on

152 brains from the Montreal Neurological Institute (MNI) with a voxel-size resolution of 3 x 3 x

8

3 mm, using a deformation field estimated on a coregistered structural image. Finally, the

normalized images were smoothed by convolution with an 8 mm full-width at half-maximum

Gaussian kernel.

General Linear Model. Preprocessed images from each task were analysed using the

General Linear Model (GLM) framework implemented in SPM, consistently with previous

studies using the same paradigms1 5 7 8. For the Empathy for Pain task, trial time onsets from

each of the four conditions were modelled with a delta function. Additionally, for each

condition we also included an additional vector in which participants Response Times were

modulated parametrically5. For the BART, we modelled with a delta function all inflation events

(in which participants were prompted a decision), with the probability of explosion fed as

additional parametric regressor. Furthermore, we also modelled positive (win) and negative

(loose) feedbacks as separate regressors7 8. For the SHAME we modelled, separately for each

player, all trials in which participants were prompted with a judgment, as well as all kinds of

feedback (correct, incorrect painless, incorrect painful) with separate delta function1.

For all tasks, we accounted for putative habituation effects in neural responses of each

condition by using the time-modulation option implemented in SPM, which creates a regressor

in which the block/trial order is modulated parametrically. Furthermore, each regressor was

convolved with a canonical hemodynamic response function and associated with its first order

temporal derivative. To account for movement-related variance, we included six differential

movement parameters as covariates of no interest. Low-frequency signal drifts were filtered

using a cutoff period of 128 sec. Serial correlations in the neural signal were accounted through

exponential covariance structures, as implemented in the ‘FAST’ option of SPM12. Global

9

scaling was applied, with each fMRI value rescaled to a percentage value of the average whole-

brain signal for that scan.

Functional contrasts, testing differential parameter estimates images associated with

one experimental condition vs. the other were then fed in a second level, one-sample t-test

using random-effect analysis. Similarly, parameter estimates of conditions of interest were fed

to univariate linear regressions, using one of the three clinical measures of interest as

predictors. Effects were considered significant if exceeded p < 0.05, family-wise correction for

multiple comparisons at the cluster level (with an underlying height threshold of p < 0.001,

uncorrected). In addition, we report also effects surviving p < 0.05 small volume corrected for

masks of interests, defined through by previous studies in which independent lay populations

were engaged in the same studies implemented here1 5 8.

Region of interest masks. For each task, we identified an inclusive mask,

comprehending only those coordinates of theoretical interest, as obtained by reanalysing data

from independent researches employing the same paradigms1 5 8, under similar preprocessing

and modelling settings of the current study. The only exceptions were related to those datasets

in which a field map image was not available, for which no unwrapping was applied during the

preprocessing stage. Consequently, in these cases, the deformation field for the normalization

was estimated directly from the functional images (instead from a coregistered structural

volume), to minimize the impact of geometric distortions related the magnetic field

inhomogeneity9. Furthermore, as all these previous datasets were acquired with long repetition

time (> 2 sec), serial autocorrelations were accounted with standard first-order autoregressive

AR(1) model (as opposed to the FAST option for rapid sequences).

10

For the Empathy for Pain task, we reanalysed the data from Corradi-Dell’Acqua et al.5.

As this previous study employed larger database of pictures than ours, we considered as

conditions of interest those parameters estimated on the same sub-selection of images which

were used for the present study (the remaining images were modelled as separate conditions

of no interest), which were then used to identify brain regions associated with the contrast

Painful – Control (Table S1). For the BART, we reanalysed the study of Schonberg et al.8 (freely

available at https://openneuro.org/datasets/ds000001/). This study included an additional

control condition characterized by the inflation of balloon without risk of money loss8, which

was modelled in the first level as separate regressor of no interest. In keeping with results

reported by the original study8, we took into consideration regions implicated in the contrast

monetary loss – implicit baseline (in this specific dataset, no differential effect between

monetary loss – win were reported; see Table S2). Finally, for the SHAME, we reanalysed the

data from Koban et al.1 and selected features implicated in one’s erroneous performance with

painful outcome, compared with others’ erroneous trials with painful outcome (One’s – Others’

painful errors; see Table S3). In all these mask, we followed previous studies implementing

similar multivariate regression on whole brain data10 11, and excluded the coordinates in

occipital cortex that may be driven by distinctive visual features rather than on the information

of interest.

Multivariate Regression

We used Least Absolute Shrinkage and Selection Operator (LASSO)10–13 and Random Forest (RF)

regression14 to identify distributed patterns of activity across brain that could be predictive of

nurses’ professional behaviour in Emergency Department.

https://openneuro.org/datasets/ds000001/

11

Feature Selection. For each task, we identified an inclusive mask, comprehending only

those coordinates of theoretical interest, as obtained from independent datasets in which lay

participants were engaged in similar paradigms than those used here1 5 8 (see above).

LASSO. For each of these independently-defined masks, we extracted the neural

activation associated with corresponding tasks in the present study. The resulting data matrixes

(e.g., for the Empathy for Pain Task, 33 nurses x 1077 voxels) were then fed into a LASSO

routine (lasso function from Matlab R2013b) to identify which components were jointly

predictive of nurses’ behaviour in their clinical practice (as recorded during the preceding 15

months). To optimize the modelling, but at the same time insure its generalizability to new

data, the LASSO regression was conducted in two nested 10-fold cross-validation loops. The

first (inner) was aimed at optimizing regularization hyper-parameter λ. The second (outer) was

aimed at predicting professional behaviour of a portions of nurse by a model optimized (also in

its hyper-parameters) on out-of-sample nurses.

Random Forest Regression. The same data matrixes were also fed to the regression

routines implemented in the Matlab-based RF toolbox14 15. This analysis involves the

implementation of decision trees, which perform recursive partitioning of the neural (feature)

space to lead to a non-linear predictive model. As decision tree-based models are susceptible to

small perturbations in the dataset, the variance in estimated prediction function was reduced

by the RF algorithm through 1000 bootstrap resampling of the original dataset, each of which

provides its own contribution (or vote) to the final prediction14. Generalizability of the

regression was conducted through a 10-fold cross-validation loop, in which the model

optimized in a portion of nurses was tested on out-of-sample nurses.

12

Permutation Analysis. The proficiency of the LASSO and RF procedures was assessed by

estimating the mean squared error (MSE), reflecting the deviation between nurses’ actual

behaviour and the behaviour estimated from the brain activity. This value was considered

significant if lower than the 5th percentile of the distribution of 1000 MSEs obtained by

rerunning the whole procedure on permuted datasets.

13

Supplementary Results

Empathy for Pain Task

Tables S4-5 report all behavioural (Table S4) and neuroimaging (Table S5) results associated

with this task. Behavioural effects include on-line accuracy and response time, but also post-

experimental rating sessions. Consistent with a previous study using the same paradigm5,

negative images (both painful and painless arousing) were associated with longer response

times, greater arousal, and lower accuracy, valence and familiarity, with respect to their

tailored controls (paired t-test: │t│ ≥ 1.94, p (1-tailed) ≤ 0.030; Wilcoxon sign-rank test: │Z│ ≥

2.38, p ≤ 0.017). In addition, painful images were associated with higher ratings of harm/pain,

than both their controls and painless arousing images (│t│ ≥ 7.84, p < 0.001; │Z│ ≥ 4.25, p <

0.001). However, unlike in our previous study on lay population, painful images were rated by

emergency nurses as more familiar, less negatively-valenced, and less arousing than painless

arousing images (the same for their corresponding controls – │t│ ≥ 2.40, p ≤ 0.027; │Z│ ≥ 2.24, p

< 0.025).

We then tested whether the behavioural responses to painful images could be

predictive of the three clinical indexes of interest. For this purpose we took both online (median

response times and accuracy) and offline (post-scanning ratings) measures for the condition of

interest (displayed in Table S4), and subjected them to massive univariate linear regression,

which led to no significant effects (│r│ ≤ 0.28, n.s.). When feeding all six measures to

multivariate regression using the same routines used for the analysis of brain data, we found

that a reliable prediction of the treatment application could be obtained using RF decision trees

(see Table S11). None of the other two indexes could be reliably predicted.

14

Balloon Analog Risk Task (BART)

Tables S6-7 report all behavioural (Table S6) and neuroimaging (Table S7) results associated

with this task. Behavioural data refer to the number of inflation in win trials, median response

times, the amount of money gained in the overall session, and the median subjective rating. For

each of these four measures, a linear relationship with the clinical indexes of interest was

tested with no significant results (│r│ ≤ 0.30, n.s.). Furthermore, when feeding all four

behavioural measures to multivariate regression using the same LASSO and RF approaches

employed for the analysis of brain signal, we found no reliable prediction (see Table S11).

Social Harm Avoidance Monitoring Experiment (SHAME)

Tables S8-9 report all the behavioural effects associated with the SHAME. Post-scanning ratings

obtained from all four kinds of errors suggest that nurses felt greater empathic pain, but also

greatest sadness, when observing an error with painful (vs. painless) outcome. Instead, greater

shame, guilt and anger were reported when nurses committed themselves an error (regardless

of its painful/painless outcome) relative to when they observed the confederate in the scanner

committing an error.

We then tested whether the behavioural responses to SHAME could be predictive of the

three clinical indexes of interest. For this purpose we took the ratings of pain, guilt, shame, fear,

sadness and anger (acquired in the post-scanning session) associated with One’s Painful Errors

(see Table S9). Furthermore we also took into consideration three online measures from when

the subject was playing the task in the MRI (see Table S8): the median response times,

accuracy, and median trial difficulty (as difficulty was automatically adapted according to

15

participants’ performance, the median trial difficulty throughout the experimental session is an

indirect measure of how challenging the task was for each subject). For each of these nine

measures, a linear relationship with the clinical indexes of interest was tested with no

significant results (│r│ ≤ 0.31, n.s.). When feeding all 9 measures to multivariate regression

using the same routines used for the analysis of brain data, we found that a reliable prediction

of the Documentation Rate could be obtained using RF decision trees (see Table S11). None of

the other two indexes could be reliably predicted.

Finally, Table S10 reports the regions involved when participants observed harmful

consequences of their own errors, relative to the condition in which pain was self-caused by the

colleague outside the scanner. We assessed whether these responses could be influenced by

the personal/professional relationship between the pair of nurses engaged in the task. Personal

closeness was assessed by the IOS questionnaire4 as implemented in the post-scanning debrief,

whereas professional closeness was assessed by calculating the absolute difference in age and

years of experience between the two nurses engaged in the task (no difference reflects

stronger similarity in professional status than a large difference). We then run a univariate

linear regression analysis, in which the neural responses to one’s painful errors were fitted

against each of these three measures (IOS, age difference, experience differences). No

significant effects of personal/professional closeness were found.

16

Supplementary References

1. Koban L, Corradi-Dell’Acqua C, Vuilleumier P. Integration of error agency and representation of others’ pain in the anterior insula. J Cogn Neurosci 2013; 25: 258–72

2. Corradi-Dell’Acqua C, Tusche A, Vuilleumier P, Singer T. Cross-modal representations of first-hand and vicarious pain, disgust and fairness in insular and cingulate cortex. Nat Commun 2016; 7: 10904

3. Sharvit G, Corradi-Dell’Acqua C, Vuilleumier P. Modality-specific effects of aversive expectancy in anterior insula and medial prefrontal cortex. Pain 2018; 159: 1529–1542

4. Aron A, Aron EN, Smollan D. Inclusion of Other in the Self Scale and the structure of interpersonal closeness. J Pers Soc Psychol 1992; 63: 596–612

5. Corradi-Dell’Acqua C, Hofstetter C, Vuilleumier P. Felt and Seen Pain Evoke the Same Local Patterns of Cortical Activity in Insular and Cingulate Cortex. J Neurosci 2011; 31: 17996–8006

6. Feinberg DA, Moeller S, Smith SM, et al. Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging. PLOS ONE 2010; 5: e15710

7. Rao H, Korczykowski M, Pluta J, Hoang A, Detre JA. Neural correlates of voluntary and involuntary risk taking in the human brain: an fMRI Study of the Balloon Analog Risk Task (BART). NeuroImage 2008; 42: 902–10

8. Schonberg T, Fox CR, Mumford JA, Congdon E, Trepel C, Poldrack RA. Decreasing ventromedial prefrontal cortex activity during sequential risk-taking: an FMRI investigation of the balloon analog risk task. Front Neurosci 2012; 6: 80

9. Calhoun VD, Wager TD, Krishnan A, et al. The impact of T1 versus EPI spatial normalization templates for fMRI data analyses. Hum Brain Mapp 2017; 38: 5331–42

10. Chang LJ, Gianaros PJ, Manuck SB, Krishnan A, Wager TD. A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect. PLoS Biol 2015; 13: e1002180

11. Krishnan A, Woo C-W, Chang LJ, et al. Somatic and vicarious pain are represented by dissociable multivariate brain patterns. eLife 2016; 5: e15166

12. Wager TD, Atlas LY, Lindquist MA, Roy M, Woo C-W, Kross E. An fMRI-Based Neurologic Signature of Physical Pain. N Engl J Med 2013; 368: 1388–97

13. Zunhammer M, Geis S, Busch V, Eichhammer P, Greenlee MW. Pain modulation by intranasal oxytocin and emotional picture viewing — a randomized double-blind fMRI study. Sci Rep 2016; 6: 31606

17

14. Breiman L. Random Forests. Mach Learn 2001; 45: 5–32

15. Jaiantilal A. Classification and regression by randomforest-matlab [Internet]. 2009. Available from: https://code.google.com/archive/p/randomforest-matlab/

16. Friston KJ, Worsley KJ, Frackowiak RSJ, Mazziotta JC, Evans AC. Assessing the significance of focal activations using their spatial extent. Hum Brain Mapp 1993; 1: 210–20

17. Chumbley JR, Friston KJ. False discovery rate revisited: FDR and topological inference using Gaussian random fields. NeuroImage 2009; 44: 62–70

18

Supplementary Tables

Table S1

Region of Interest mask for the Empathy for Pain Task. Regions included in both univariate and multivariate analysis defined from independent data5. The table lists the regions displaying differential activity for the contrast Painful – Control.

SIDE Coordinates

T(27) Cluster size X y z

Painful – PainfulControl

Anterior Insula R 39 29 -4 3.93 66*

Inferior Frontal Gyrus R 42 38 5 6.21

Middle/Posterior Insula R 42 5 -7 6.85 260***

Amygdala R 21 -4 -16 7.25

Anterior Insula L -33 23 -1 5.97

292*** Middle/Posterior Insula L -36 -4 8 4.26

Amygdala L -21 -4 -19 5.29

Supramarginal R 60 -22 38 8.56 301***

Postcentral Gyrus R 63 -16 29 7.08

Supramarginal L -54 -25 35 7.07 132***

Precentral Gyrus R 45 8 26 5.82 137***

***p < 0.001; **p < 0.01; *p < 0.05 family-wise corrected for the whole brain

19

Table S2

Region of Interest mask for the Balloon Analog Risk Task. Regions included in both univariate and multivariate analysis defined from independent data8. The table lists the regions displaying differential activity for the contrast monetary loss – implicit baseline.

SIDE Coordinates

T(15) Cluster size x Y z

Monetary Loss – Implicit Baseline

Anterior Insula R 42 23 -4 7.74 184***

Ventral Insula R 30 14 -19 4.74

Anterior Insula L -39 17 -1 5.30 156**

Ventral Insula L -33 14 -16 6.20

Pre-central Gyrus R 45 11 32 6.54 167**

Middle Frontal Gyrus R 42 23 47 5.62

Inferior Frontal Sulcus R 45 35 23 4.98 67*

Thalamus/Midbrain M -5 -31 37 5.77 85*

*p < 0.05 family-wise corrected for multiple comparisons at the cluster level

20

Table S3

Region of Interest mask for SHAME. Regions included in both univariate and multivariate analysis defined from independent data1. The table lists the regions displaying differential activity for the contrast One’s – Others’ Painful Errors. All clusters are displayed with a height threshold corresponding to p < 0.001 (uncorrected), and survive FWE16 or FDR17 correction for multiple comparisons for the whole brain at the cluster level.

SIDE Coordinates

T(15) Cluster size x y z

One’s – Others’ Painful Errors

Anterior Insula R 45 14 -7 5.21 58†

Putamen R 30 5 -1 5.22 71*

Anterior Insula L -39 11 -4 3.91 114**

Putamen L -21 8 5 5.60

Superior Frontal Gyrus R 36 38 53 5.38 140***

Superior Frontal Gyrus L -21 53 26 6.60 123**

Cerebellum R 27 -49 -28 5.24 59*

Anterior Middle Cingulate Cortex M 6 23 23 7.49 121**

Supplementary Motor Area M -9 14 58 7.70 111**

***p < 0.001; **p < 0.01; *p < 0.05 family-wise corrected for the whole brain; † p < 0.05

false-discovery-rate corrected for the whole brain

21

Table S4

Behavioural data from the Empathy for Pain Task. For each measure of interest, the average value associated with the four conditions is displayed together with 95% confidence intervals.

Painful Control Arousing ArousingControl

Resp. Times (ms) 1426 [1341, 1539] 1362 [1273, 1445] 1441 [1357, 1551] 1329 [1255, 1409]

Accuracy (%) 70.97 [64.02, 75.57] 83.97 [73.62, 89.61] 72.17 [64.81, 76.68] 81.55 [72.54, 87.06]

Arousal 6.15 [5.27, 7.04] 1.81 [1.48, 2.29] 7.01 [6.45, 7.64] 2.71 [2.24, 3.24]

Valence -0.73 [-1.20, -0.25] 1.23 [0.94, 1.69] -2.11 [-2.42, -1.78] 0.90 [0.65, 1.22]

Familiarity 5.29 [4.45, 6.12] 8.72 [8.27, 9.12] 2.24 [1.82, 2.75] 4.63 [4.05, 5.20]

Pain 8.92 [8.44, 9.28] 2.23 [1.58, 3.28] 3.26 [2.16, 4.69] 1.39 [1.13, 1.90]

22

Table S5

Neural Activations for the Empathy for Pain Task. Regions displaying differential activity for the

contrast Painful – Control Images, and increased activity for Painful Images with nurses’

Documentation Rate. All clusters survive correction for multiple comparisons for the whole

brain at the cluster level 16, or small-volume correction for a region of interest mask (described

in Table S1).

SIDE Coordinates

T(32) Cluster size X Y Z

Painful – Control Images

Middle/Posterior Insula R 42 -7 -1 6.64 256***

Amygdala R 24 -4 -16 8.39

Middle/Posterior Insula L -39 -1 -7 3.89 96*

Amygdala L -21 -7 -13 7.15

Inferior Frontal Gyrus R 45 38 11 8.71 133**

Inferior Frontal Gyrus L -42 32 14 6.94 116**

Precentral Gyrus R 48 8 26 6.87 160**

Precentral Gyrus L -45 5 23 7.38 167**

Inferior Temporal Gyrus R 51 -55 -10 9.23

2983***

Fusiform Gyrus R 30 -49 -13 9.07

Calcarine Gyrus R 18 -94 -4 10.02

Intraparietal Sulcus R 27 -64 44 8.04

Supramarginal/Postcentral Gyrus R 63 -22 26 9.39

Inferior Temporal Gyrus L -45 -55 -7 7.86

Fusiform Gyrus L -30 -49 -16 8.90

Calcarine Gyrus L -21 -94 -5 10.24

Periaqueductal Grey/Midbrain M -3 -31 -4 6.68

Intraparietal Sulcus L -21 -64 44 5.24 87*

Supramarginal/Postcentral Gyrus L -63 -25 32 8.20 133**

Painful Images*Documentation Rate

Postcentral Gyrus R 60 -16 32 4.50† 18

Middle Occipital Gyrus R 36 -73 14 4.51 89*

***p < 0.001; **p < 0.01; *p < 0.05 family-wise corrected for the whole brain; † p < 0.05

family-wise corrected for small volume

23

Table S6

Behavioural data from the Balloon Analog Risk Task. In keeping with previous studies, we measured the average number of inflations in each trial (excluding trials associated with negative outcome), the response times associated with each choice, and the overall money gained during the experimental session. Furthermore, we also considered subjects’ median anxiety rating collected along the whole experimental session. For each measure of interest, the average value is displayed together with 95% confidence intervals.

# inflations Response Times (ms) Money gained (CHF) Anxiety [0-100]

6.03 [5.79, 6.26] 584 [536, 643] 50.08 [45.23, 55.02] 31.25 [25.33, 37.42]

24

Table S7

Neural Activations for the Balloon Analog Risk Task. Regions displaying differential activity for

the contrast monetary loss – win, and increased activity for Monetary Loss with nurses’ CI Rate.

SIDE Coordinates

T(31) Cluster

size x Y z

Monetary Loss – Win

Anterior Insula R 39 17 -4 9.79

603*** Ventral insula R 39 -1 -16 10.97

Posterior Insula R 39 -10 -7 6.36

Anterior Insula L -36 20 -7 11.74

493*** Ventral Insula L -27 8 -22 9.54

Posterior Insula L -39 -4 -13 5.74

Temporo-Parietal Junction R 60 -40 20 4.65 68*

Middle Occipital Gyrus L -30 -88 14 4.93 168***

Inferior Occipital Gyrus L -45 -73 2 5.51

Precentral/Postcentral Gyrus R 39 -16 50 3.97

1025*** Supplementary Motor Area M 9 8 62 8.19

Middle Cingulate Cortex M -6 17 35 7.78

Calcarine Cortex R 18 -64 14 5.06

1802***

Lingual Gyrus R 27 -61 -7 8.74

Middle Temporal Gyrus R 46 -64 -4 5.09

Calcarine Cortex L -15 -70 11 6.10

Fusiform Gyrus L -27 -67 -7 7.67

Thalamus M 9 -28 -7 8.08

Monetary Loss*CI Rate

Ant. Insula/Inf. Frontal Gyrus R 24 32 -10 4.96 170***

Mid. Insula-Opercular Junction R 21 -13 14 5.06 69*

Putamen R 30 -4 11 4.98

Ant. Insular-Opercular Junction L -27 29 20 4.90 156**

Mid. Insula-Opercular Junction L -36 5 17 5.87 128**

Hippocampus R 30 -28 -7 4.55 104**

Thalamus R 21 -34 -1 4.21

Angular Gyrus R 39 -52 20 4.45 63*

Middle Occipital Gyrus R 45 -76 23 4.95 120**

Middle Occipital Gyrus L -33 -64 17 5.02 88*

Cerebellum R 12 -37 -34 5.49 759***

Cerebellum L -42 -58 -37 4.91

25

Middle Cingulate Cortex M -6 -4 29 5.33 261***

***p < 0.001; **p < 0.01; *p < 0.05 family-wise corrected for the whole brain.

Table S8

Behavioural data from the SHAME: online measures. For each measure of interest, the average value associated with the four conditions is displayed together with 95% confidence intervals.

Difficulty (dots diff) Accuracy (%) Resp. Times (ms) Pain Threshold (deg)

Subject in the scanner (self)

4.75 [4.57, 4.88] 52.01 [47.28, 54.27] 928 [848, 1001] ---

Subject outside the scanner

(other) 4.84 [4.61, 4.97] 51.85 [50.11, 55.17] 953 [877, 1024] 48.31 [47.78, 48.75]

26

Table S9

Behavioural data from the SHAME: post-scan rating measures. For each measure of interest, the average value associated with the four kinds of errors is displayed together with 95% confidence intervals. “Self” refers to the case in which the participant in the MRI scanner made a mistake, whereas “Other” refers to the case in which the participants in the MRI scanner observed the confederate making a mistake. “Pain” and “NoPain” refer to errors with painful and painless outcomes respectively.

Pain Fear Shame Guilt Sadness Anger

Self-Pain 2.61 [2.07, 3.35] 1.93 [1.50, 2.50] 2.81 [2.29, 3.30] 3.77 [3.13, 4.23] 2.42 [1.93, 2.94] 3.26 [2.68, 3.80]

Other-Pain 2.61 [2.03, 3.39] 2.45 [1.91, 3.03] 1.48 [1.22, 1.97] 1.42 [1.16, 1.85] 2.29 [1.76, 2.88] 1.74 [1.35, 2.29]

Self-NoPain 1.64 [1.28, 2.07] 1.45[1.16, 1.94] 2.52 [2.03, 3.03] 2.84 [2.28, 3.37] 1.74 [1.37, 2.23] 2.58 [2.06, 3.10]

Other-NoPain 1.45 [1.16, 1.85] 1.64 [1.30, 2.17] 1.42 [1.16, 1.91] 1.42 [1.14, 1.94] 1.84 [1.45, 2.34] 1.35 [1.07, 1.85]

27

Table S10

Neural Activations for the SHAME. Regions displaying differential activity for the contrast One’s

– Other’s Painful Errors, and increased activity for One’s Painful Errors with nurses’ CI Rate. All

clusters are displayed with a height threshold corresponding to p < 0.001 (uncorrected), and

survive correction for multiple comparisons for the whole brain, or small-volume correction for

a region of interest mask (described in Table S3).

SIDE Coordinates

T(30) Cluster

size x y z

One’s – Others’ Painful Errors

Anterior Middle Cingulate Cortex M -3 26 29 4.35† 25

One’s Painful Errors*CI Rate

Middle Frontal Gyrus L -33 8 53 6.79 104*

Anterior Middle Cingulate Cortex M 3 44 14 5.94 131**

**p < 0.01 *p < 0.05 family-wise corrected for the whole brain; † p < 0.05 family-wise

corrected for small volume

28

Table S11

Multivariate modelling of behavioural data. For each task, we modelled brain activity with multivariate regression based on LASSO and Random Forest (RF) approaches. The analysis of each task is described in terms of number of features fed to the multivariate regression, as well as measures of model’s proficiency (Mean Square Error [MSE]) at predicting each of the three indexes from the delegated analgesia protocol. Significant predictions are highlighted in bold, and significance cut-offs (5th percentile of a permutation-based MSE distribution) are displayed in squared brackets. Full details in Supplementary Methods.

Task # Features Algorithm Docum Rate CI Rate Treatment App.

From the neuroimaging session

Pain Images 6 LASSO 2.48 [2.23] 1.79 [1.55] 1.73 [1.49] ∙10-3

RF 2.28 [2.13] 1.92 [1.44] 1.29* [1.37]

Monetary Loss 4 LASSO 3.44 [3.10] 1.81 [1.71] 2.12 [1.87]

RF 3.91 [2.91] 1.93 [1.60] 2.24 [1.79]

One’s Painful Errors 9 LASSO 3.23 [3.14] 1.93 [1.68] 2.18 [1.98]

RF 2.86* [2.97] 2.10 [1.61] 2.30 [1.82]

* error lower than chance at p < 0.05

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