Perception of social inclusion/exclusion and response inhibition in adolescents with past suicide attempt: a multimodal task-based fMRI study

Abstract

The occurrence of suicidal behaviors increases during adolescence. Hypersensitivity to negative social signals and deficits in cognitive control are putative mechanisms of suicidal behaviors, which necessitate confirmation in youths. Multidomain functional neuroimaging could enhance the identification of patients at suicidal risk beyond standard clinical measures. Three groups of adolescents (N = 96; 78% females, age = 11.6–18.1) were included: patients with depressive disorders and previous suicide attempts (SA, n = 29); patient controls with depressive disorders but without suicide attempt (PC, n = 35); and healthy controls (HC, n = 32). We scanned participants with 3T-MRI during social inclusion/exclusion (Cyberball Game) and response inhibition (Go-NoGo) tasks. Neural activation was indexed by the blood-oxygenation-level dependent (BOLD) of the hemodynamic response during three conditions in the Cyberball Game (“Control condition”, “Social Inclusion”, and “Social Exclusion”), and two conditions in Go-NoGo task (“Go” and “NoGo” blocks). ANCOVA-style analysis identified group effects across three whole-brain contrasts: 1) NoGo vs. Go, 2) Social inclusion vs. control condition, 3) Social inclusion vs. control condition). Normalized contrasts in significant clusters were used to train a support vector machine-based classifier with a stratified 5-fold cross-validation, and diagnostic performance was assessed. In line with previous adult studies, we found that SA had lower activation in the left insula during social inclusion vs. control condition compared to PC and HC. We also found that SA compared to PC had higher activity in the right middle prefrontal gyrus during social exclusion vs. control condition, and in bilateral precentral gyri during NoGo vs. Go conditions. Task-related measures (Self-reported emotional reactivity in the Cyberball Game, response times and number of errors in the Go-NoGo Task) did not discriminate between groups. Moreover, while clinical data (Self-reported depression and impulsivity scores) yielded moderate accuracy (Accuracy: 70%/ Area Under Curve: 0.81), activity during Go-NoGo (81%/0.90), Cyberball Game (89%/0.90), or a combination (88%/0.95) significantly enhanced identification of past suicidal behaviors. In conclusion, adolescent suicidal behaviors are likely associated with neural alterations across multiple domains. Alterations in the processing of social perception and response inhibition may underlie the development of suicidal crises, from onset with social triggers to susceptibility to act out. Neuroimaging should be further tested as a tool to predict suicidal behavior.

Full text

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Perception of social inclusion/exclusion and
response inhibition in adolescents with past suicide
attempt: a multimodal task-based fMRI study
Fabrice Jollant (  fabrice.jollant@universite-paris-saclay.fr )
Paris-Saclay University https://orcid.org/0000-0001-5809-4503
Anthony Gifuni
Stanford University https://orcid.org/0000-0003-2418-5591
Fabricio Pereira
Mallar Chakravarty
Douglas Mental Health Institute, Montréal, Canada
Martin Lepage
McGill University https://orcid.org/0000-0003-4345-6502
Henry Chase
University of Pittsburgh
Marie-Claude Geoffroy
Université de Montréal
Eric Lacourse
Mary Phillips
University of Pittsburgh School of Medicine https://orcid.org/0000-0003-4958-1291
Gustavo Turecki
McGill University https://orcid.org/0000-0003-4075-2736
Johanne Renaud
Article
Keywords: Adolescent, Attempted Suicide, Functional Neuroimaging, Social Exclusion, Executive Function
Posted Date: January 5th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-2271723/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License

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Abstract
The occurrence of suicidal behaviors increases during adolescence. Hypersensitivity to negative social
signals and decits in cognitive control are putative mechanisms of suicidal behaviors, which necessitate
conrmation in youths. Multidomain functional neuroimaging could enhance the identication of
patients at suicidal risk beyond standard clinical measures. Three groups of adolescents (N = 96; 78%
females, age = 11.6–18.1) were included: patients with depressive disorders and previous suicide
attempts (SA,
n
= 29); patient controls with depressive disorders but without suicide attempt (PC,
n
= 35);
and healthy controls (HC,
n
= 32). We scanned participants with 3T-MRI during social inclusion/exclusion
(Cyberball Game) and response inhibition (Go-NoGo) tasks. Neural activation was indexed by the blood-
oxygenation-level dependent (BOLD) of the hemodynamic response during three conditions in the
Cyberball Game (“Control condition”, “Social Inclusion”, and “Social Exclusion”), and two conditions in Go-
NoGo task (“Go” and “NoGo” blocks). ANCOVA-style analysis identied group effects across three whole-
brain contrasts: 1) NoGo vs. Go, 2) Social inclusion vs. control condition, 3) Social inclusion vs. control
condition). Normalized contrasts in signicant clusters were used to train a support vector machine-
based classier with a stratied 5-fold cross-validation, and diagnostic performance was assessed. In
line with previous adult studies, we found that SA had lower activation in the left insula during social
inclusion vs. control condition compared to PC and HC. We also found that SA compared to PC had
higher activity in the right middle prefrontal gyrus during social exclusion vs. control condition, and in
bilateral precentral gyri during NoGo vs. Go conditions. Task-related measures (Self-reported emotional
reactivity in the Cyberball Game, response times and number of errors in the Go-NoGo Task) did not
discriminate between groups. Moreover, while clinical data (Self-reported depression and impulsivity
scores) yielded moderate accuracy (Accuracy: 70%/ Area Under Curve: 0.81), activity during Go-NoGo
(81%/0.90), Cyberball Game (89%/0.90), or a combination (88%/0.95) signicantly enhanced
identication of past suicidal behaviors. In conclusion, adolescent suicidal behaviors are likely
associated with neural alterations across multiple domains. Alterations in the processing of social
perception and response inhibition may underlie the development of suicidal crises, from onset with
social triggers to susceptibility to act out. Neuroimaging should be further tested as a tool to predict
suicidal behavior.
1. Introduction
Adolescent suicidal behaviors are increasingly recognized as a leading public health concern [1, 2].
Suicide ranks as the second or third leading cause of youth mortality worldwide [3], and rates have
tended to increase in recent years in the USA and Canada [4, 5]. Alarmingly, non-fatal suicidal ideation
and behaviors during adolescence have a prevalence of 12% and 4%, respectively [6, 7]. Adolescence is
associated with an abrupt increase in suicidal thoughts and behaviors (STB) [7, 8], which later increases
the risk for psychological diculties [9] and STB in adulthood [10]. During the recent COVID-19 pandemic,
an important increase in STB in adolescents but not in adults has been observed in several countries [11,

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12]. Despite the burden of adolescent STB, our understanding of adolescent STB neural basis remains
rudimentary, limiting preventive and therapeutic approaches.
Clinical evaluation to predict suicidal behavior notoriously lack accuracy, and the use of historical and
self-reported data provides marginal improvement to prognostic models [13, 14]. Psychiatric research has
emphasized the importance of "biomarkers" as a stepping stone toward developing predictive tools and
facilitating personalized care [15]. With its ability to identify brain structural and functional alterations
correlating with psychopathology, neuroimaging has emerged as a potential tool to provide biomarkers
associated with suicide risk [16]. Most neuroimaging studies have probed brain alterations associated
with suicide risk in adult populations [17–20], with studies in adolescents recently increasing in number
[21]. To date, a few neuroimaging studies reported on single functional neural modalities, identifying
disparate processes linked with STB, including facial emotional processing [22], self-referencing [23], and
social cognition [24]. However, single neuroimaging modalities might be insucient to capture fully the
underlying neural predisposition to adolescent STB. In the current study, we jointly examined two distinct
functional domains that might both contribute to adolescent suicidal risk from a painful social situation
to the emotional and behavioral response: sensitivity to social inclusion/exclusion and response
inhibition.
Several theories of suicide recognize that the feeling of social disconnection is an important precipitant
of STB [25–27]. From puberty to adulthood, social relationships become increasingly salient for
adolescents [28]. Numerous epidemiological and clinical evidence suggests that social exclusion, peer
victimization, and cyberbullying contribute to adolescent STB [29–32]. One of the most validated
paradigms to investigate the neural correlates associated with social exclusion is the fMRI Cyberball
Game [33, 34], a virtual ball-tossing game simulating inclusion and exclusion [35]. While most research
has examined the neural correlates of social exclusion among healthy subjects [34, 36], fewer studies
have addressed patients with STB. A study in adult females showed altered activity in the left insula and
supramarginal gyrus in patients with past suicide attempt vs. control patients during exclusion vs.
inclusion [37]. One study in adolescents with depression and non-suicidal self-injury, a behavior
overlapping with suicidal behaviors [38], showed elevated activation of the medial and ventrolateral
prefrontal cortex during social exclusion relative to inclusion (Groschwitz et al., 2016). Finally, in a sample
of adolescents with suicide ideation, another study [39] revealed that the subgroup of adolescents with
recent suicide attempts had elevated activity in the anterior cingulate cortex and lateral prefrontal cortex
(PFC) during any social interaction, including periods of inclusion and exclusion. Given the heterogeneity
of these samples and their small sizes, these ndings need further exploration and replication. It also
remains unclear whether these anomalies related to social processes correlate with other cognitive
processes related to suicidality, such as cognitive control.
Cognitive control, the ability to exibly orient attention to goal-relevant information and discard irrelevant
information, has been implicated in the suicidal transition from suicidal ideas to acts [40–42]. Cognitive
control is notably instrumental to ecient regulation of emotions [43]. In adolescents, however, the
association between cognitive control and suicidal behaviors is still unclear [44], which might be

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explained by at least two reasons. First, suicidal behaviors are notoriously heterogeneous, with some
suicidal attempts done with minimal premeditation, while others are planned over several days or weeks
[45]. Second, adolescence is characterized by rapid developmental shifts in cognitive control [46].
Adolescents’ cognitive skills are typically characterized by higher variability between assessments and
lacking the exibility seen in adulthood [47]. Substantial empirical evidence shows that immaturity in
cognitive control systems predisposes youth to impulsive sensation-seeking and riskier decision-making
[48, 49]. A common paradigm to investigate neural correlates of cognitive control is the Go-NoGo Task
[50], which is not only sensitive to age-related maturation [51] but also disorder-related alterations [52].
One previous study in a small sample [53] found that adolescents with past suicide attempts exhibited
decreased neural activation in the right anterior cingulate cortex compared to depressed controls, but not
compared to healthy controls.
In the current study, the Cyberball Game and Go-NoGo tasks during fMRI in three groups: depressed
adolescents with past suicidal behaviors, depressed adolescents without past suicidal behaviors, and
healthy controls. We expected to nd neural disturbances in each functional domain in adolescents with
past suicidal behaviors compared to control groups, including in regions involved in emotional and
mentalizing processes, such as the anterior cingulate cortex, the striatum, the insula, or the precuneus;
and in cognitive control regions, such as the lateral prefrontal cortex [17]. Additionally, we explored the
potential diagnostic utility of neuroimaging disturbances using either linear regression or supervised
machine learning classiers. We posited that each task-based neuroimaging modality would provide
superior diagnostic accuracy compared to clinical data such as depressive symptoms or self-reported
impulsivity. Furthermore, we expected that combining functional domains would be better than single
domains and that using supervised machine learning methods would improve the identication of
adolescents with past suicidal behaviors.
2. Methods
2.1 Participants
From September 2012 to January 2019, three groups of adolescents (N = 104) aged 11 to 18 years were
recruited: 1) Adolescents with a depressive disorder and a history of at least one suicide attempt (SA;
n
=
30); 2) Adolescents with a depressive disorder without a lifetime history of suicide attempt (Patient
controls, PC;
n
= 38); and 3) Adolescents without a personal history of psychiatric disorder or a suicide
attempt (healthy controls, HC;
n
= 36).
A suicide attempt was dened as any self-injurious behavior carried out with the intent to die [54]. This
did not include aborted suicide attempts (i.e., halted by oneself), interrupted suicide attempts (i.e., halted
by someone), or non-suicidal self-injuries. Depressive disorders, dened by DSM-IV criteria [55], included
major depressive disorders, dysthymia, and depressive disorder not otherwise specied.

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Participants with a depressive disorder (SA and PC) were primarily recruited from a third-line psychiatric
clinic specialized in adolescent depressive disorders (at the Douglas Mental Health University Institute,
Montreal, Canada), with additional cases recruited from aliated community clinics. Access to these
specialized psychiatry services is limited to patients aged below 18 years. HC were recruited from the
community through advertisements posted in schools, local clinics, youth centers, and groups of parents
on social media. HC were initially screened through a phone interview by a trained research assistant to
exclude adolescents with a history of psychiatric disorders or a suicide attempt.
Exclusion criteria included neurological disorders (e.g., epilepsy, brain tumor), traumatic brain injury (> 1
min loss of consciousness, neuroimaging anomaly, or persistent post-concussive symptoms), autistic
spectrum disorder, bipolar disorders, psychotic disorders, intelligence quotient (IQ) less than 70,
pregnancy, and any contraindication for magnetic resonance neuroimaging (e.g., dental apparatus). A
family history of suicidal behavior was an additional exclusion criterion for the HC group (but not the SA
or PC groups) because of evidence that some neural phenotypes are transmitted within families with
suicide histories [56, 57]
Neuroimaging data were not available for 5 participants (2 PC and 3 HC) either due to claustrophobia (n
= 1), refusal (n = 2), or corrupted or irretrievable data (n = 2). Three additional participants (1 SA, 1 PC, 1
HC) were excluded from analyses due to poor data quality (see below for quality check criteria). Hence,
after exclusion and missing data, the nal analyzed sample (N = 96) included 29 SA, 35 PC, and 32 HC.
The research protocol was approved by the Douglas Institute Research Ethics Board (Protocol 12/20).
Consent was provided by at least one parent or legal guardian, and all adolescents assented to the
experiment. All participants were compensated when completing the assessment procedure (50 CAD).
2.2 Assessment
Assessments were completed in two or three visits, which included clinical and neuropsychological
assessments and a neuroimaging session performed within the same week. Suicidal behaviors were
assessed with the Suicide History Questionnaire [see Appendix in 58], a clinical interview with a child and
adolescent psychiatrist (JR), and cross-validation with notes from patients’ medical les (reviewed by
AJG). Psychiatric disorders were characterized by the Kiddie Schedule for Affective Disorders and
Schizophrenia—Present and Lifetime [59, K-SADS-PL; 60]. Current depressive symptoms were measured
with the Beck Depression Inventory-II [BDI-II, 61]. Full-scale IQ was measured using the Wechsler
Intelligence Scale for Children − 4th edition [WISC-IV; 62] in participants younger than 17 years and with
the Wechsler Adult Intelligence Scale − 4th edition [WAIS-IV; 63] for participants aged 17–18 years. Self-
reported impulsivity was assessed with the Barratt Impulsivity Scale [BIS-11; 64].
2.3 Imaging procedure
2.3.1 Description of tasks

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Cyberball Game.
The Cyberball Game is a computer ball-tossing game seeking to simulate social
exclusion [33, 35, 65]. Through instructions given in the scanner, participants were invited to play online
with two other players, which were presented as real, although they were pre-programmed. Participants
were represented by a pair of hands in the lower center portion of the screen, while other players were
represented by animated cartoons next to a named prole picture in each upper corner of the screen. After
receiving the virtual ball, subjects could throw it to either player by selecting one of two buttons on a
remote control in their right hand. The Cyberball Game consisted of three rounds lasting 2:30 minutes
following xed order: 1) Passive viewing, 2) Social inclusion, 3) Social exclusion. In the rst round
(“Passive viewing”), participants were told that a connection problem prevented them from joining the
game and that they were to watch the two other players exchange the ball. This rst round serves as a
baseline for the following rounds and aims to increase the realism of the task. In the second round
(“Social inclusion”), participants receive the ball randomly one-third of the time and can throw back the
ball to either player. The third round (“Social exclusion”) starts as the second one, but after 60 seconds
and without any warning, the other players stop throwing the ball toward the participant. The total
duration of the task was 10 minutes. After the scanning session, the distress induced by social exclusion
was documented with the Need-Threat Scale [NTS; 66, 67], which includes ratings of self-esteem (e.g., “I
felt liked”), belongingness (e.g., “I felt rejected”), meaningful existence (e.g., “I felt invisible”), and control
(e.g., “I felt powerful”), scaled from 1 (not at all) to 5 (very much). Items were reverse-coded when
appropriate and averaged to create a composite score. After completing the NTS, participants were
debriefed about the task.
Go-NoGo Task
. Cognitive inhibition was tested with a classical version of the Go-NoGo task [68],
implemented in E-Prime 2.0.10.182 (USA). The task comprised a block-design paradigm in which
participants were shown a total of 144 letters across 6 blocks for a total duration of 6.1 minutes. Trials
were distributed in 2 block types: 3 “Go” blocks (Block A), and 3 “NoGo” blocks (Block B), presented in an
ABBAAB order. In “Go” blocks, participants were instructed to press a button in response to visually
presented letters using their right index nger as quickly as possible. In “NoGo” blocks, participants were
instructed to avoid pressing the button to a non-target letter (letter X), while still pressing the button in
response to target letters (i.e., letters other than X). Each block (either “Go” or “NoGo”) started with a 20-
second blank-screen resting period and 5-second instructions followed by 24 trials. Each trial consisted of
a black xation cross on a white screen followed by a letter. The duration of the xation cross varied
between 700, 900, 1100, or 1300 milliseconds, randomized to prevent habituation, with 6 trials of each
duration resulting in an average of 1000 milliseconds. Letters (target or non-target) were presented for
500 milliseconds on a white screen. In each block (“Go” or “NoGo”), 12 target letters (50%) and 12 non-
target letters (50%) were presented in a pre-determined pseudorandomized order. For all trials, reaction
time (time between stimulus onset and button press response), omission errors (i.e., not responding to a
target error), and commission errors (i.e., responding to a non-target letter during a “NoGo” Block) were
recorded. The sum of omission errors is usually interpreted as indicative of attentional or speed decits,
whereas commission scores would reect inhibition decits [69]
2.3.2 Imaging Acquisition and preprocessing

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Magnetic resonance imaging (MRI) scans were acquired at the Douglas Cerebral Imaging Centre using a
Siemens Magnetom Trio (Tim System 3T, MR B17) scanner equipped with a 12-channel head coil. The
complete scanning procedure (see Supplements -
Scanning Protocol
and
Neuroimaging acquisition
for
full description) included a high-resolution T1 anatomical scan (repetition time [TR] = 2300 ms; echo time
[TE] = 2.98 ms; inversion time [TI] = 900 ms; ip angle [FA] = 9°; eld of view [FOV] = 256 mm; voxel size
[VS] = 1x1x1 mm; matrix =: 256x256, acquisition time [T] = 9.23 min), task-based functional MRI with a
socio-emotional paradigm (Cyberball Game; TR = 3000ms; TE = 25ms; FA = 90°, FOV = 200 mm; matrix =
64x64; VS = 3.1x3.1x4.0 mm; T = 5.5 min) and a cognitive control paradigm (Go-NoGo task; TR = 2090ms;
TE = 30 ms ; FA = 90°; FOV = 224 mm; matrix: 64x64; voxel = 3.5x3.5x3.5 mm; T = 6.27 min). The
functional images were quality controlled and preprocessed using fMRIPrep 1.4.1 [70–72].
2.4 Statistical analyses
2.4.1 Demographic and clinical differences
Statistical analyses were performed in R v3.6.0 [73], implemented in Rstudio v1.1.383 [74]. Clinical and
behavioral continuous data were compared across groups with 3-way ANOVAs, and Tukey’s HSD Post
Hoc tests for bivariate comparisons. Categorical group data were compared with chi-square tests.
Correlations between clinical measures and brain activity extracted from signicant clusters were
computed with Spearman’s rank-order correlation. The alpha level was set a priori at p = 0.05 with
Bonferroni correction based on the number of signicant clusters in across both fMRI tasks.
2.4.2 Neuroimaging analysis
See the Supplements -
Anatomical data preprocessing
and
Functional data preprocessing
for the
complete neuroimaging analysis procedure. In brief, we derived within-subject contrasts maps of interest
using a block-designed models. For the Cyberball Game, we calculated an inclusion and an exclusion
contrast, using the “Passive viewing” condition as the control condition. For the Go-NoGo Task, the
contrast of interest was NoGo vs. Go blocks, reecting activity when the task demands cognitive
inhibition as opposed to a simple motor response. Between-participants whole-brain analyses were
conducted with 3dMVM in AFNI version 19.3.11 [75]. Given the effect of age, sex, and IQ on neural activity
and cortical anatomy [76, 77], the group analyses controlled for these covariates. Signicance thresholds
were set at voxel level p-uncorrected < 0.005. To minimize false-positives, a more stringent level at p <
0.001 was also tested. The resulting statistical maps were corrected for multiple comparisons at p-
corrected < 0.05 using AFNI’s 3dClustsim algorithm. Noise volume was simulated assuming a spatical
autocorrelation function (ACF) given by a mixed-model of the form a*exp(-r*r/(2*b*b))+(1-a)*exp(-r/c),
where a, b, c parameters were estimated by 3dFWHMx using residual timeseries leftovers post-GLM
tting. The cluster size surviving whole brain correction, using a grey-matter mask, was determined to be
k > 517 for Cyberball Game and k > 504 for Go-NoGo. Post-hoc pairwise analyses were only conducted in
clusters that were found to be signicant at the three-group comparison level in order to limit false
positives. Normalized cluster contrast were extracted for correlational analyses with clinical, behavioral,
and neuroimaging data.

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2.4.3 Diagnostic accuracy provided by multidomain
functional MRI
Classication models identifying participants with past suicide attempts were computed with a support
vector machine (SVM) based-classier, a supervised learning method already tested in suicide prediction
[78]. We adopted an SVM-based model using a linear kernel, which calculates high-dimension linear
decision boundaries (“hyperplanes”) using the “max-margin principle”. For comparison, we computed a
conventional classication model using a standard logistic regression. The analysis aimed to classify
suicidal (SA) vs. non-suicidal participants (PC and HC). Two baseline models were computed: 1) Only
sociodemographic data (age, sex, IQ), 2) Sociodemographic and clinical data (age, sex, IQ, BDI, and BIS
total scores). Models with extracted fMRI contrast coecients from single or combined tasks were built
on top of each baseline model for a total of 8 models. Models only used contrast coecients from
signicant clusters at the three-group level at p < 0.005. The performance of each model was estimated
via a stratied 5-fold cross-validation. Classication accuracy and receiver operating characteristic curves
(ROC) area under the curve (AUC) were averaged across each fold (k = 5) and compared with baseline
models with Welch’s t-tests. Training and testing classication models were implemented in Scikit-learn
(v0.23.1; [79]), a machine learning toolkit for python.
3. Results
3.1 Group sociodemographic and clinical characteristics
Sociodemographic and clinical characteristics of the three groups are presented in Table1. Groups were
broadly similar, notably SA and PC. See sTable 1 for medication use and dose ranges.

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Table 1
Comparison of sociodemographic, clinical and task-related behavioral variables between the three groups
Suicide
Attempt Patient
Controls Healthy
Controls Group comparison
(N = 29) (N = 35) (N = 32)
    χ2/ F-stat p-
value Post-hoc
comparison
Sex      
Female, n (%) 25 (86) 28 (80) 22 (73) 2.8 (df =
2) 0.24 -
Age (years)      
Mean (sd) 16.3 (1.0) 16.0 (1.5) 15.3 (1.4) 4.5 (df =
2,93) 0.01* SA > HC
Race/Ethnicity      
Asian, n (%) < 5 < 5 < 5 0.3 (df =
2) 0.9 
Black, n (%) < 5 0 < 5 1.7 (df =
2) 0.4 
White, n (%) 22 (76) 25 (71) 19 (69) 2.1 (df =
2) 0.4 
First Nations, n (%) < 5 5 (14) < 5 6.4 (df =
2) 0.04* PC > SA,HC
Latinx/Hispanic, n
(%) < 5 0 0 4.7 (df =
2) 0.09 
Multiethnic, n (%) < 5 < 5 8 (25) 6.9 (df =
2) 0.03 HC > SA,PC
Parental Education      
Elementary, n (%) 8 (28) < 5 0 (0) 11.6 (df
= 2) 0.003* SA > PC,HC
High School, n (%) 5 (18) 5 (14) 7 (23) 0.8 (df =
2) 0.7 -
College, n (%) < 5 < 5 6 (19) 1.6 (df =
2) 0.4 -
University, n (%) 12 (43) 24 (69) 18 (58) 4.8 (df =
2) 0.09 -
Intelligence Quotient (WISC/WAIS)

Page 10/27
Suicide
Attempt Patient
Controls Healthy
Controls Group comparison
(N = 29) (N = 35) (N = 32)
Total, Mean (sd) 102.3
(15.9) 108.4
(13.7) 111.0
(12.7) 2.45 (df
= 2,93) 0.1 -
Beck Depression Scale - II
Total, Mean (sd) 30.2
(12.7) 25.6
(12.3) 5.9 (5.8) 46.4 (df
= 2,93) <
0.001* SA,PC > HC
Barratt Impulsivity
Scale      
Total, Mean (sd) 73.4 (9.1) 65.26
(11.1) 58.9
(11.0) 10.4 (df
= 2,93) <
0.001* SA,PC > HC
Attention, Mean (sd) 20.5 (3.4) 17.4 (3.5) 14.8 (4.3) 15.5 (df
= 2,93) <
0.001* SA > PC >
HC
Motor, Mean (sd) 21.4 (4.6) 19.5 (4.3) 18.4 (3.7) 3.0 (df =
2,93) 0.06 -
Non-Planning, Mean
(sd) 30.5 (3.6) 28.4 (5.6) 25.7 (5.7) 6.0 (df =
2,93) 0.003 SA,PC > HC
NSSI Lifetime history      
NSSI lifetime history,
n (%) 25 (86) 22 (63) 5 (16) 31.2 (df
= 2) <
0.001* SA,PC > HC
Psychiatric diagnosis
MDD, n (%) 14 (48) 25 (71) - 2.7 (df =
1) 0.1 -
Dysthymia, n (%) 5 (17) 8 (23) - 0.1 (df =
1) 0.8 -
DDNOS, n (%) 11 (38) < 5 - 4.8 (df =
1) 0.03* SA > PC
Anxiety
disorder/PTSD, n (%) 12 (41) 18 (51) - 0.6 (df =
1) 0.4 -
Eating Disorder, n (%) < 5 < 5 - 0.1 (df =
1) 0.8 -
ADHD, n (%) 9 (32) < 5 - 2.8 (df =
1) 0.09 -
Psychotropic medication
Antidepressant, n (%) 15 (52) 20 (57) - 6.0 (df =
1) 0.5 -

Page 11/27
Suicide
Attempt Patient
Controls Healthy
Controls Group comparison
(N = 29) (N = 35) (N = 32)
Mood Stabilizer, n (%) < 5 < 5 - 0.02 (df
= 1) 0.9 -
Low-Dose
Neuroleptic, n (%) 12 (41) 8 (23) - 1.7 (df =
1) 0.2 -
Benzodiazepine, n (%) < 5 < 5 - 0 (df = 1) 1 -
Stimulant, n (%) 8 (28) < 5 - 6.1 (df =
1) 0.01* SA > PC
Task-related variables      
Go-NoGo : Response
Time (ms)      
Go Blocks, Mean (sd) 269 (36) 257 (34) 278 (31) 3.4 (df =
2,92) 0.04* PC < HC
NoGo Blocks, Mean
(sd) 301 (50) 284 (33) 310 (34) 4.2 (df =
2,92) 0.02* PC < HC
Go-NoGo : Errors
(Count)      
Omission Errors, Go
Blocks 9.8 (10.1) 8.1 (6.2) 10.1 (9.0) 0.5 (df =
2,92) 0.6 -
Omission Errors,
NoGo Blocks 3.5 (4.9) 2.9 (2.6) 3.0 (3.0) 0.3 (df =
2,92) 0.7 -
Commission Errors,
NoGo Blocks 6.7 (4.4) 8.4 (3.4) 6.2 (3.3) 3.7 (df =
2,92) 0.03* PC > HC
Cyberball - Need-
Threat Scale      
Belonging, Mean (sd) 12.7 (4.3) 14.1 (3.2) 16.3 (3.9) 6.6 (df =
2,87) 0.002 SA,PC < HC
Self-Esteem, Mean
(sd) 13.1 (4.4) 13.6 (3.9) 18.2 (4.0) 14.5 (df
= 2,87) <
0.001* SA,PC < HC
Signicant Existence,
Mean (sd) 12.8 (5.2) 14.4 (3.5) 17.4 (3.2) 10.0 (df
= 2,87) <
0.001* SA,PC < HC
Sense of Control,
Mean (sd) 10.1 (3.7) 11.0 (2.9) 13.4 (4.0) 7.5 (df =
2,87) <
0.001* SA,PC < HC
Total, Mean (sd) 48.7
(14.8) 53.3
(10.4) 65.5
(12.5) 7.7 (df =
2,87) <
0.001* SA,PC < HC

Page 12/27
Among SA, the average time between the last suicide attempt and the scanning session was 13.5 months
(standard deviation (sd) = 12.0, range=[0.9–42.3]). The average number of suicide attempts was 2.0 (1.6,
[1–7]), and the average age at the rst suicide attempt was 14.3 years (1.3, [10.6–16.6]).
Participants with missing neuroimaging data (N = 8) did not differ from the rest of the sample (N = 96) in
terms of age, sex, or race/ethnicity but not parental education (sTable 2).
3.2 Cyberball Game
Both patient groups had lower post-task NTS scores than HC, suggesting higher emotional impact in
patients in general unrelated to a history of suicide attempt (see Table1). Correlations between NTS
scores and sociodemographic and clinical variables are presented in sTables 3 and 4.
Neuroimaging results for the Cyberball Game are presented in Table2 and Fig.1 (at voxel-correction p <
0.005) and sTable 5 (at p < 0.001). For the inclusion vs. control condition contrast, a signicant group
effect was detected in the left insula. Post-hoc analyses showed lower insular activity in SA compared to
both PC and HC. For the exclusion vs. control condition contrast, a signicant group effect was found in
three clusters located in the left and right inferior frontal gyrus, and the right middle/superior frontal
gyrus. In SA vs. PC, lower activation was found in the right inferior frontal gyrus and higher activation in
the right middle/superior frontal gyrus. In SA vs. HC, lower activation was found in the left and right
inferior frontal gyri. Finally, in PC vs. HC a lower activation was found in the left inferior frontal gyrus and
the right middle/superior frontal gyrus. At a more stringent voxel threshold (p < 0.001), ndings remained
signicant in the left insula (Inclusion contrast) and the right superior frontal gyrus (Exclusion contrast).

Page 13/27
Table 2
Group difference in fMRI activity elicited by the Cyberball Game and Go-NoGo Task (3 contrasts, voxel
threshold p < 0.005; Cluster correction p < 0.05)
   Size Peak voxel Statistics 
Region Brodmann
Area Side Voxels
(n) Coordinates
(MNI) (F/t-stat) Direction of
effects
Contrast: Inclusion - Control
Insula †BA13 L 629 [-36,-6,8] F = 12.3 SA < PC, SA <
PC, PC = HC
Contrast: Exclusion - Control
Inferior Frontal Gyrus BA45 L 660 [-50,24,14] F = 11.9 SA = PC, SA
< HC, PC < HC
Middle/Superior
Frontal Gyrus †
BA9, BA10 R 593 [34, 40, 32] F = 11.5 SA > PC, SA
= HC, PC < HC
Inferior Frontal Gyrus BA10 R 520 [33, 44, 4] F = 9.9 SA < PC, SA <
HC, PC = HC
Contrast: NoGo vs. Go
Precentral Gyrus †BA6, BA9 R 3,409 [37, 1, 27] F = 15.6 SA > PC, SA >
HC, PC = HC
Precentral Gyrus †BA6 L 1,549 [-47,-6,32] F = 9.2 SA > PC, SA
= HC, PC < HC
Inferior Frontal Gyrus BA46 L 690 [-39,31,8] F = 6.9 SA = PC, SA
> HC, PC > HC
Fusiform Gyrus BA20,BA36 R 512 [46,–35,–27] F = 7.7 SA = PC, SA
> HC, PC = HC
† Cluster signicant at voxel threshold p < 0.001; Cluster correction p < 0.05, cf. sTable 5 for details.
R = right, L = left; SA: Patient with past suicide attempt; PC: Patient controls; HC: Healthy controls
Correlations between signicant brain cluster activity and sociodemographic and clinical variables are
presented in sTables 3 and 4. Left Insula activity in the inclusion-minus contrast correlated signicantly
total score on the NTS (ρ = 0.29, p corrected = 0.04), specically with Belonging (ρ = 0.33, p corrected =
0.008) and Signicant Existence (ρ = 0.34, p corrected = 0.007). Activity exclusion-related clusters did not
correlate with NTS Score (sTable 6).
3.3 Go-NoGo Task
Behavioral performance is presented in Table1. PC showed lower reaction times and more commission
errors than HC. There was no difference between SA versus PC or HC. Correlations between behavioral
performance and sociodemographic and clinical variables are presented in sTables 3 and 4.

Page 14/27
Multivariate analysis, controlling for age, sex, and IQ, indicated group differences for the NoGo vs. Go
contrast in four signicant large clusters (see Table2 and Fig.1 at voxel-correction p < 0.005 and sTable 4
at p < 0.001): the left and right precentral gyri, the left inferior frontal gyrus, and the right fusiform gyrus.
Pairwise post-hoc comparisons showed higher activity in bilateral precentral gyri in SA vs. PC. Increased
activity was also found in SA vs. HC in the left inferior frontal gyrus, the right precentral gyrus, the right
fusiform gyrus. Finally, PC vs. HC had lower activity in the left precentral gyrus and higher activity in the
left inferior frontal gyrus. Sensitivity analysis controlling for medication status, psychiatric diagnoses, or
head motion (average framewise displacement) did not change the pattern of neural activity related to
group effects. At a more stringent voxel threshold (p < 0.001), ndings remained signicant in both
precentral clusters.
Correlations between signicant brain cluster activity and sociodemographic and clinical variables are
presented in sTables 3 and 4. Activity in the left precentral gyrus correlated signicantly with response
times in NoGo Blocks. No other correlation between group-discriminating clusters in the Go-NoGo Task
correlated with behavioral outcomes on the task (sTable 7).
The correlation matrix between activation contrasts in the Cyberball Game and the GoNo-Go Task is
presented in Supplementary Fig.1. Activation in the left insula (Inclusion contrast) correlated negatively
(r=-0.32, p uncorrected = 0.00002) with activity in the right precentral gyrus (GoNo-Go contrast).
3.4 Diagnostic accuracy provided by multimodal task-based
fMRI
Classication performance is reported in Table3 and ROC curves of all compared conditions are shown
in Fig.2. Classication models attempting to discriminate adolescents with past suicidal behaviors from
the whole sample were built hierarchically using sociodemographic, clinical, single, or combined
neuroimaging data. In line with our hypothesis, combining neuroimaging data extracted from signicant
clusters (Go-NoGo: Right and left precentral gyri, left inferior frontal gyrus, fusiform gyrus; Cyberball
Game: Left insula, left inferior frontal gyrus, right middle/superior frontal Gyrus, right inferior frontal
gyrus) from both tasks improved classication performance, both in terms of classication accuracy and
AUC: Classication accuracy increased by approximately 20% and AUC increased by 0.25 compared to
baseline models, indicating a signicant enhancement in diagnostic power. The SVM classier using
combined neuroimaging data without clinical data provided the highest classication accuracy (92.6%)
and highest AUC (0.96). Classication accuracy and AUC were not signicantly different between models
using combined vs. single neuroimaging modalities. Of note, clinical variables (BDI and BIS) did not
signicantly enhance classication on their own, and their inclusion did not enhance models with
neuroimaging features. Finally, the SVM-based classier did not signicantly surpass conventional
regression models.

Page 15/27
Table 3
Classication performance of sociodemographic data, clinical data, and functional neuroimaging
markers (single task or combined tasks) with either a logistic regression or a Support Vector Machine-
based learning model.
Statistical Model (5-fold Cross-Validation)
Variables Logistic Regression Support Vector Machine
Accuracy (%/sd) AUC (sd) Accuracy (%/sd) AUC (sd)
1. Baseline169.1 (6.2) 0.70 (0.09) 70.2 (2.3) 0.50 (0.23)
2. Clinical270.3 (9.57) 0.80 (0.10) 70.3 (8.3) 0.81 (0.10)*
3. GNG382.0 (5.2)* 0.87 (0.04)* 79.8 (5.0)* 0.87 (0.04)*
4. CB483.0 (6.0)* 0.90 (0.06)* 83.0 (6.0)* 0.89 (0.05)*
5 GNG + CB592.6 (2.5)** 0.95 (0.03)** 92.6 (2.5)** 0.96 (0.03)*
6. Clinical + GNG 83.0 (5.0)* 0.91 (0.04)** 80.9 (6.2)* 0.90 (0.05)*
7. Clinical + CB 87.19 (4.35)**†0.93 (0.05)** 89.4 (4.6)**†0.90 (0.06)*
8. Clinical + GNG + CB 86.3 (7.1)*†0.95 (0.05)**†88.4 (9.0)*†0.95 (0.06)*†
Footnotes
:
1. Baseline variables: Age, sex, and IQ
2. BDI and BIS Scores, with baseline variables
3. Go-No-Go fMRI BOLD extracted signals in signicant ROIs, with baseline variables
4. Cyberball fMRI BOLD extracted signals in signicant ROIs, with baseline variables
5. Combined Go-No-Go and Cyberball fMRI BOLD extracted signals in signicant ROIs, with baseline
variables
* p < 0.05 Compared to Baseline model (1)
** p < 0.005 Compared to Baseline model (1)
† p < 0.05 Compared to Clinical model (2)
Abbreviations
: CB: Cyberball Game, AUC: Area Under Curve, GNG: Go-NoGo Task; sd: Standard
Deviation,
4. Discussion
The objective of the current study was to identify task-related fMRI markers associated with past suicide
attempts, assessing two putatively relevant aspects of adolescent suicidality: perception of social

Page 16/27
inclusion/exclusion and cognitive control. In summary, our study revealed two major ndings. First, we
observed a set of neural disturbances during both social inclusion/exclusion and cognitive inhibition
tests in adolescent suicide with past suicide attempt in comparison to both control groups. These
alterations were mainly located in the left insula and prefrontal cortex for social perception and
interaction; and motor and prefrontal cortices for inhibition of action (at a more stringent level of
correction). Interestingly, behavioral performances (self-report of emotional feelings during the Cyberball
Game and reactions times and omission/commission errors at the Go-NoGo task) were similar between
both patient groups suggesting that functional MRI may yield ner properties than neuropsychological
tests and questionnaires to discriminate patients with and without a personal history of suicide attempt.
Second, activity in these signicant brain clusters enhanced the identication of a past history of suicidal
attempt, outperforming clinical and sociodemographic variables.
During the Cyberball Game, adolescent SA displayed distinct neural functional alterations in relation to
both inclusion and exclusion conditions, as compared to PC and HC. In contrast to previous studies
(Harm et al. 2019), we found that the set of anatomical regions distinguishing suicidal adolescents
depended on the valence of social circumstances (i.e., inclusion or exclusion). In the inclusion situation,
SA showed reduced neural activity in the left insula, a region involved in the salience network [80].
Decreased activity in left insula (although more posterior) during inclusion vs. control condition at the
Cyberball Game was also found in adult females with past attempts (Olié et al, 2017). In our study, lower
insular activity was associated with lower feeling of belongingness and having a less meaningful
existence during perceived social exclusion. These two factors are in line with psychological risk factors
associated with suicide [81], somewhat paralleling the concepts of “thwarted belongingness” and
“perceived burdensomeness” found in the Interpersonal Theory of Suicide [27]. Thus, it is possible that
the lower activation of the left insula during inclusion in SA may interfere with the normally reinforcing
feelings associated with inclusive social interactions [82]. These ndings may also reect a lower ability
of SA to feel connected to others. It may be hypothesized that in SA this may both limit their
pleasantness of being with others and also their will to seek help when in diculty. Reduced disclosure of
suicidal ideas has indeed been associated with increased risk of social isolation and suicide attempt in
adolescents [83, 84]. Of note, lower insular activity during inclusion was also correlated with higher
depressive state, suggesting a signicant effect of the negative mood state that may be stronger in
individuals at risk of suicide.
In contrast to inclusion, social exclusion in SA was associated with increased activity in right
middle/superior frontal gyrus (surviving at a more stringent correction level) and lower activity in the right
inferior frontal gyrus compared to PC (and in the left inferior frontal gyrus compared to HC although
activity also seems to be lower in SA than PC). These differences in prefrontal cortex activation point
toward possible diculties in regulating emotions and behaviors [85, 86]. Of note, activity in the left (but
not right) inferior frontal gyrus during exclusion correlated negatively with depressive symptoms,
suggesting that depression might again interfere with regulatory prefrontal control during social
interaction. Previous studies using the Cyberball Game also observed alterations in prefrontal cortex
activation during exclusion in adolescents with a history of suicide attempt or non-suicidal self-injury [39,

Page 17/27
87]. Social processing abnormalities in the prefrontal cortex have also previously been described in SA,
for instance while viewing angry faces in adults [88, 89] and adolescents [22]. Interestingly, while
neuroimaging during exclusion was able to discriminate patients with and without a history of suicidal
acts, this was not the case for the social threat questionnaire, questioning the limits of self-reports.
During the Go-NoGo Task, adolescent SA mainly exhibited greater bilateral activity in the precentral gyri –
primary motor brain regions - compared to PC (surviving more stringent correction). We did not observe
behavioral differences between SA and PC, only between patients and HC. Activity in these motor regions
during the Go-NoGo task did not correlate with impulsivity measured with self-questionnaires. However,
they were negatively correlated with response time in our study, suggesting that increased activity may
lead to slower response times. The Go-NoGo task specically demands inhibition of a prepotent motor
response [68]. Increased activation in SA may therefore reect excessive activity to achieve the same
outcome, and, therefore indirectly inecient functioning. Furthermore, these ndings corroborate an
earlier adolescent study (Pan et al. 2011) which also found that neural activity patterns during response
inhibition discriminated between SA and PC, but that behavioral measures did not. Hence, functional
markers might be more sensitive than neuropsychological measures.
We found that the activities of left insula during inclusion and right precentral gyrus during inhibition
were correlated, suggesting a functional connection between the networks underlying social
perception/interactions and cognitive inhibition. Overall, it could be hypothesized that in situation of
stress and emotional disturbances (e.g., a depressive episode), this inecient functioning of brain
networks encompassing both the left insula and motor and precentral regions may translate into a lower
feeling of social connectedness, lower ability for self-disclosure and help-seeking and inecient
emotional and behavioral regulation facilitating both the emergence of suicidal ideas and acting out.
Recent studies have shown that suicidal behaviors are associated with the dysfunctional connectivity of
various brain regions [90].
Finally, exploratory results from our study indicate that considering functional alterations related to
cognitive and socio-emotional processing signicantly enhanced the identication accuracy of
adolescents with past suicidal behaviors from PC and SA. According to two classication performance
metrics (ROC AUC, classication accuracy), neuroimaging modalities signicantly improved baseline
models that used sociodemographic data with or without clinical variables. It should be noted, however,
that while the most accurate model was provided by the combination of both fMRI modalities without
clinical variables (> 90% accuracy, > 95% AUC), the difference between combined models and single
modalities was marginal. An important caveat is that the diagnostic validity of these functional markers
were tested on the same cohort from which they were derived, which can lead to an overestimation of
their effect [91]. Their combination within this sample might not be as effective as their combined use in
an independent sample, where each neuroimaging modality might separately be less diagnostically
ecacious [92]. We also found that an SVM-based learning method did not outperform conventional
logistic regressions. While non-linear kernels with SVM, or other supervised learning methods such as
decision trees might have provided better t [78], logistic regressions already provided high diagnostic

Page 18/27
utility. Deep learning methods are promising methods to integrate neuroimaging and clinical data to
assess suicide risk, but they require large-size data for training [93].
We must highlight a few limitations of the current study. Firstly, even with a substantial number of
participants with past suicidal behaviors, we may have lacked the statistical power to detect effects at
more conservative levels of correction [94]. Our main results are based on a voxel-wise threshold of p <
0.005, which carries a higher likelihood of false-positives than a typically recommended voxel-wise
threshold of p < 0.001 [95]. However, supplementary analyses at a threshold of p < 0.001 yielded
signicant and concordant results across all contrasts. Besides, these analyses did not account for the
variability of cluster-size threshold across all brain regions. While our strategy to simulate noise volume
with a mixed-model ACF is deemed acceptable, newer methods using randomization and permutation are
considered more accurate at controlling false positive rates [96]. Second, participants had a wide age
range (11–18), which probably introduced developmental variability in our analysis despite controlling for
age. We previously reported structural brain differences across ages in this sample [58]. Third, despite
recruiting in a relatively homogeneous second-line psychiatric clinic, several clinical heterogeneities still
characterized our sample, including features of suicidal behaviors, psychiatric comorbidities, and
environmental factors. These heterogeneities may prevent generalization to different populations. Finally,
while controlling for medication status did not change our ndings, we did not account for more complex
pharmacological effects, such as dose effects, duration of treatment, and interactions.
In conclusion, our study revealed discrete sets of functional alterations associated with suicidal
behaviors during social interactions and cognitive inhibition, two important features that underlie the
development of a suicidal crisis. These alterations may furthermore enhance diagnostic identication of
adolescents with previous suicide attempts. These ndings highlight the complex mechanisms
underlying suicidal behaviors. The capacity of neural measures to predict suicidal behavior beyond
clinical and sociodemographic variables will have to be tested in large-scale longitudinal studies.
Abbreviations
SA: Patient with Past suicide Attempt; PC : Patient Controls; HC : Healthy Controls; df : degree of freedom;
sd : standard deviation; ADHD: Attention Decit Hyperactivity Disorder; DDNOS: Depressive Disorder Not
Otherwise Specied; MDD: Major Depressive Disorder; NSSI: Non-suicidal Self-injury; WISC: Wechsler
Intelligence Scale for Children; WAIS: Wechsler Adult Intelligence Scale.
Declarations
Acknowledgments
This research was nanced byManulife Research Fund in Teen Depression, which supports theManulife
Centre for Breakthroughs in Teen Depression and Suicide Preventionin Montréal, Canada. AJG was
supported theFonds de Recherche du Québec –Santé (FRQS/MSSS Resident Physician Health Research

Page 19/27
Career Training Program). MMC was supported by a Junior 2 Research Scholar Salary from
theFQRS.ML was supported by a Research Chair from FRQS and from a James McGill Professorship.
MCG holds a Canada Research Chair Tier-2 and both JR and MCG are supported by the Fonds de
recherche du Québec – Société et Culture research team on youth suicide prevention.The content is
solely the responsibility of the authors.
We thank Daysi Zentner, Geneviève Laurent, and Léa Perret for their assistance with data collection and
organization. Finally, we thank the participants and their families participating in this study as well as the
clinicians involved with adolescents and their families (Theodora Mikedis, Jean-Chrysostome Zanga,
Didier Blondin-Lavoie).
Conict of interest statement
The authors declare no conict of interest.The funding agencies played no role in the design and
conduct of the study; collection, management, analysis, and interpretation of the data; and preparation,
review, or approval of the manuscript.
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Figures

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Figure 1
Group Differences on the Go-NoGo Task and Cyberball Game fMRI (voxel threshold p<0.005, Cluster
corrected p<0.05)
Caption:

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A. Go-NoGo Task (NoGo vs Go Contrast)
B. Cyberball Game (Inclusion vs Control Contrast)
C. Cyberball Game (Exclusion vs Control Contrast)
Figure 2
Diagnostic accuracy provided by Socio-Demographic Data, Clinical Variables, and Neural Contrast
Extracted from Signicant Clusters in the Go-NoGo Task and the Cyberball Game fMRI - Receiver Operant
Curves (ROC) Based on k=5 Stratied Cross-Validation
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