Clinical trial NCT03074409
Conditions: Interoception
Keywords: Oxytocin
Sponsor: University of Electronic Science and Technology of China
Start date: 2017-03-02; Completion date: 2017-09-12
Enrollment: 83 participants; Allocation: RANDOMIZED; Intervention model: PARALLEL

Brief summary:
This study is designed to investigate oxytocin's effects on interoception and whether these effects vary as a function of different emotional valence.

Detailed description:
The present study will use a double-blind, between-subject, placebo-controlled design to investigate the modulatory effect of oxytocin on interoception and whether this effect will vary as a function of emotional valence. In Experiment 1, subjects will perform a heartbeat detection task and the group differences between oxytocin and placebo groups of the interoceptive accuracy and neural activity in the core interoceptive network will be investigated. In Experiment 2, subjects will be asked to watch emotional faces while perform the heartbeat detection task. Investigators will examine whether oxytocin's effects on interoception will be varied as a function of different emotional valence.

Arms: arm 1: intranasal administration of oxytocin arm 2: intranasal administration of the same ingredient except oxytocin
Interventions: intervention 1: oxytocin intervention 2: placebo
Intervention descriptions: intervention 1: self-administration of oxytocin (nasal spray) intervention 2: self-administration of placebo (nasal spray)

Protocol documents:
Prot_SAP_000.pdf: 
1 
 
The Modulatory Effect of Oxytocin on Interoception 
October 29, 2018 
 
 
Study Protocol with SAP 
 
 


2 
 
Participants and treatment 
83 healthy male students (mean age = 21.35 years, SD = 2.48) participated in a 
randomized, double-blind, between-subject pharmaco-fMRI experiment where they 
received either intranasal OT (40 IU; Oxytocin Spray, Sichuan Meike Pharmacy Co. 
Ltd, China) or placebo (PLC, same ingredients other than OT). All subjects were 
right-handed and had normal or corrected-to-normal vision. None was currently 
taking any form of medication or reported a history of, or current, neurological or 
psychiatric symptoms. 8 subjects were excluded due to low quality of ECG data (6 
subjects) or failure of behavioral data acquisition (2 subjects). Thus, 37 subjects in the 
OT group and 38 subjects in the PLC group were included in the final analysis. 
Questionnaires  
To control potential confounding effects from personality traits or current mood 
states, all subjects completed Chinese versions of validated psychometric 
questionnaires before OT/PLC administration, including the Positive and Negative 
Affect Schedule (Watson et al, 1988), Autism Spectrum Quotient (Baron-Cohen et al, 
2001), Empathy Quotient (Baron-Cohen and Wheelwright, 2004), and NEO five-
factor inventory (Costa and MacCrae, 1992). Subjects were asked to fill the Positive 
and Negative Affect Schedule for 3 times with the first time after they arrived the lab 
(pre-treatment), the second time before scanning (post-treatment), and the third time 
after scan (post-scan). 
Experiment 1 


3 
 
Experiment 1 consisted of two types of task. In the heartbeat detection task 
participants were instructed to make a keypress each time they felt their heartbeat, 
while in a dot detection control task participants were instructed to make a keypress 
each time they detected a white dot appearing on the screen. Thus, the design allowed 
specific investigation of interoceptive processing by comparing attention to one’s own 
heartbeat with attention to neutral-valenced, non-social exteroceptive visual stimuli. 
There were 2 functional runs in Experiment 1 and each run consisted of 6 blocks. In 
each run, 3 heartbeat detection blocks alternated with 3 dot detection blocks with a 
jittered interval of 8-12 s. Blocks were preceded by a 2-s cue indicating the task type 
of the subsequent block. Each block lasted 30 s and the order of the two types of 
block was counterbalanced across subjects. An identical, but static white dot was also 
presented during heartbeat detection in order to maintain visual conformity across the 
two tasks. Interoceptive accuracy was calculated as the mean score across allof the 
heartbeat detection blocks using the following transformation (Pollatos et al, 2007a, 
2007b): 
1/N ∑(1 - (|recorded heartbeats - counted heartbeats|)/recorded heartbeats) 
N was the number of blocks for the heartbeat detection task. 
In the control task, to avoid subjects pressing the key regularly after repeated 
sessions the dot was presented at a varying rate of 26-40 times/30 s (mean ± standard 
deviation = 32.67 ± 5.65) across blocks. This rate was determined in accordance with 
the normal range of resting heart rate in healthy Chinese young male adults (51-96 
beats/min) (Wu et al, 2001), with a 25% random variance (Zaki et al, 2012). For 


4 
 
example, if a subject’s heart rate was 60 times/min, the dot was presented every 1.00 
± 0.25 s. To make the accuracy of the two types of task comparable, the accuracy in 
the control task was calculated in a similar way to the interoceptive accuracy using the 
following transformation: 
1/N ∑(1 - (|presented rate  - tap rate|)/presented rate) 
    The presented rate was the presentation times of the dot during each 30-s control 
block. The tap rate was the keypress numbers made by the subject during each 30-s 
control block. N was the number of blocks for the control task. 
Experiment 2 
There were 3 functional runs in Experiment 2. Each run comprised 10 blocks with 2 
blocks in each condition (happy, disgust, fearful, neutral faces and scrambled faces). 
The 10 blocks were presented in a pseudorandom order with a jittered interval of 8-12 
s. Each block consisted of 5 faces with the same expression and each face was 
displayed for 6 s. Thus, each block lasted for 30 s. Interoceptive accuracy was 
calculated as the mean score of 6 heartbeat detection blocks for each face stimuli 
using the same transformation used in Experiment 1. 
The emotional faces of 5 males and 5 females with happy, disgust, fearful and 
neutral expressions were selected from the Taiwanese Facial Expression Image 
Database (Chen and Yen, 2007). These faces were selected on the basis of high 
emotion discrimination accuracy as determined by a pilot rating study including 40 
subjects not involved in the current study. All faces were carefully edited (hair, ears 
and background information removed) to reduce confounding effects from irrelevant 


5 
 
features. The scrambled mosaics were created by dividing a neutral face into 25 parts 
and then randomly rearranging them into a mosaic-scrambled image. All faces and 
images were presented in full color at 400 × 500 pixel resolution. 
ECG Acquisition and Processing 
ECG was obtained using an ECG100C-MRI-compatible amplifier module and was 
recorded using an MP150 system running AcqKnowledge v4.2 software (BIOPAC 
Systems, Inc.) with three MRI-compatible electrodes placed on participants’ chest. 
The data was sampled at 2000 Hz and the ECG100c-MRI was set to a gain of 1000, 
with 1 Hz HP set to ON and 35 Hz LPN set to ON, in accordance with the BIOPAC 
manual. To remove the echo planar imaging (EPI) artifact, the raw ECG data was 
preprocessed in AcqKnowledge v4.2 software by performing a Comb Band Stop 
Filter (fixed at 18 Hz with a Q of 20), followed by a digital filter with a band pass 
between 0.5 and 35 Hz. Next, the RR intervals were extracted and manually corrected 
for artifacts. The resulting intervals were then analyzed and transformed into heartbeat 
number/30 s (i.e. to match the blocks in the study which were 30 s) for each condition 
using a custom Matlab script (Matlab 2013a). 
Image Acquisition and Data Analysis 
Images were collected using a 3 T, GE Discovery MR750 scanner (General Electric 
Medical System, Milwaukee, WI, USA). During each fMRI scan, a time series of 
volumes was acquired using a T2*-weighted EPI pulse sequence (repetition time, 
2000 ms; echo time, 30 ms; slices, 39; thickness, 4 mm; gap, 1 mm; field of view, 240 
× 240 mm; resolution, 64 × 64; flip angle, 90°). High-resolution whole-brain volume 


6 
 
T1*-weighted images were acquired obliquely with a three-dimensional spoiled 
gradient echo pulse sequence (repetition time, 6 ms; echo time, 2 ms; flip angle, 9°; 
field of view = 256 × 256 mm; acquisition matrix, 256 × 256; thickness, 1 mm; 
number of slices, 156) to control for any anatomic abnormalities and increase 
normalization accuracy during pre-processing. 
Brain images were processed using the SPM8 software package (Wellcome 
Department of Cognitive Neurology, London, UK, http://www.fil.ion.ucl.ac. 
uk/spm/spm8) (Friston et al, 1994). The first five images were excluded to achieve 
magnet-steady images and the remaining functional images were realigned to correct 
for head motion based on a six-parameter rigid body algorithm. After co-registering 
the mean functional image and the T1 image, the T1 image was segmented to 
determine the parameters for normalizing the functional images to Montreal 
Neurological Institute (MNI) space. Next normalized images were spatially smoothed 
with a Gaussian kernel (8 mm full-width at half maximum, FWHM).  
To further examine OT’s effects during interoceptive processing, we performed 
exploratory psychophysiological interaction (PPI) analyses using the PPI toolbox 
(McLaren et al, 2012). In brief, this entails regressing responses throughout the brain 
on the activity of one or more seed regions. The PPI corresponds to a difference in 
this regression under different (psychological) experimental levels. In the present 
study, we asked whether OT modulates the coupling between AI and other brain 
regions by comparing the (physiological) effects – from the seed region – between the 
OT and PLC groups. 


7 
 
For the brain behavior analyses, parameter estimates were extracted from 6-mm 
sphere ROIs based on the corresponding first-level contrast representing the average 
activity across face types for each subject using MarsBar toolbox (Brett et al., 2002). 
For the connectivity analysis the mean regression coefficient for each subject was 
extracted as a measure of connectivity strengths using the same contrast. Pearson 
correlations were next calculated based on the subject-specific parameter estimates 
and the corresponding interoceptive accuracy score (average accuracy across all face 
types) from each subject.  
Group Difference of Head Motion  
To examine confounding effects of head motion, we used a two-sample t-test on 
the mean framewise displacement using Jenkinson’s relative root mean square 
algorithm (Jenkinson et al, 2002) to examine the group difference of head motion. 
Results showed no significant difference of head motion between OT and PLC groups 
either in Experiment 1 (t(73) = 1.47, p = 0.145) or Experiment 2 (t(73) = 0.56, p = 
0.581). 
Results of Personality and Mood Questionnaires 
There were no significant group differences on questionnaire scores measuring 
mood, autistic traits, empathy, and personality (Table S1). Repeated-measurement 
ANOVA with time (pre-treatment vs. post-treatment vs. post-scan) as within-subject 
factor and treatment (OT vs. PLC) as between-subject factor were performed 
separately on the positive and negative mood scores (Positive and Negative Affective 
Scale). For the positive mood, results showed a significant main effect of time (F(2, 


8 
 
140) = 8.02, p = 0.001), with significant decrease of the positive mood from pre-
treatment (p = 0.004) and post-treatment (p = 0.038) to post-scan. For the negative 
mood, there was also a significant main effect of time (F(2, 140) = 34.05, p < 0.001), 
with significant decrease of negative mood from pre-treatment to post-treatment (p < 
0.001) and post-scan (p < 0.001). The negative mood decrease from post-treatment to 
post-scan was also significant (p = 0.042). However, there were no significant main 
effects of group and its interaction with time (ps > 0.394). 
 
 


9 
 
References 
Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E (2001). The autism-
spectrum quotient (AQ): Evidence from asperger syndrome/high-functioning 
autism, malesand females, scientists and mathematicians. J Autism Dev Disord 
31: 5-17. 
Baron-Cohen S, Wheelwright S (2004). The empathy quotient: an investigation of 
adults with Asperger syndrome or high functioning autism, and normal sex 
differences. J Autism Dev Disord 34: 163-175. 
Brett M, Anton JL, Valabregue R, Poline JB (2002). Region of interest analysis using 
the MarsBar toolbox for SPM 99. NeuroImage 16: S497. 
Chen LF, Yen YS (2007). Taiwanese facial expression image database. Brain Mapping 
Laboratory, Institute of Brain Science, National Yang-Ming University, Taipei, 
Taiwan. 
Costa PT, MacCrae RR (1992). Revised NEO personality inventory (NEO PI-R) and 
NEO five-factor inventory (NEO FFI): Professional manual. Psychological 
Assessment Resources. 
Friston KJ, Holmes AP, Worsley KJ, Poline JP, Frith CD, Frackowiak RS (1994). 
Statistical parametric maps in functional imaging: a general linear approach. 
Hum Brain Mapp 2:189-210. 
Pollatos O, Gramann K, Schandry R (2007a). Neural systems connecting 
interoceptive awareness and feelings. Hum Brain Mapp 28: 9-18. 
Pollatos O, Schandry R, Auer DP, Kaufmann C (2007b). Brain structures mediating 


10 
 
cardiovascular arousal and interoceptive awareness. Brain Res 1141: 178-187. 
Watson D, Clark L, Tellegen A (1988). Development and validation of brief measures 
of positive and negative affect-The PANAS scales. J Pers Soc Psychol 54: 1063-
1070. 
Wu J, Kors JA, Rijnbeek PR, Lu Z, Xu C, van Bemmel JH (2001). A survey on the 
normal range of heart rate in healthy Chinese. Chin J Cardiol 29: 369-371. 
Zaki J, Davis JI, Ochsner KN (2012). Overlapping activity in anterior insula during 
interoception and emotional experience. NeuroImage 62: 493-499. 
 
 


11 
 
Table S1. Questionnaire scores in the OT and PLC group (mean ± sd). 
Measurements 
OT 
PLC 
t-value 
p-value 
Age 
21.2±2.5 
21.5±2.4 
-0.63 
0.529 
Positive and Negative Affective Scale 1st 
 
 
 
 
- Positive 
29.9±6.0 
28.7±6.4 
0.88 
0.383 
- Negative 
16.4±5.3 
16.8±6.0 
-0.30 
0.769 
Positive and Negative Affective Scale 2nd 
 
 
 
 
- Positive 
29.1±7.9 
28.2±6.2 
0.54 
0.591 
- Negative 
13.4±4.3 
14.2±5.1 
-0.71 
0.478 
Positive and Negative Affective Scale 3rd 
 
 
 
 
- Positive 
27.4±7.7 
25.6±7.7 
1.00 
0.320 
- Negative 
12.1±3.7 
13.3±4.1 
-1.32 
0.191 
Autism Spectrum Quotient 
18.7±5.0 
19.2±5.3 
-0.43 
0.669 
Empathy Quotient 
42.0±9.1 
40.2±11.0 
0.77 
0.444 
NEO five-factor inventory 
 
 
 
 
Neuroticism 
29.6±7.7 
31.3±6.8 
-0.98 
0.333 
Extroversion 
44.4±5.4 
42.3±7.0 
1.45 
0.151 
Openness 
40.1±4.7 
40.2±5.3 
-0.09 
0.930 
Agreeableness 
41.4±4.0 
41.6±5.3 
-0.21 
0.835 
Conscientiousness 
43.6±5.3 
42.5±4.3 
1.00 
0.319 
       95% Confidence interval. 
 
 
 
 


12 
 
Table S2. Regions out of the a priori ROIs in Experiment 1 (MNI coordinates). 
Brain Region  
BA 
No. Voxels 
Peak t-value 
x 
y 
z 
Interoception > control task 
 
 
 
 
 
 
L. Precuneus 
7/18/19/31 
12747 
11.09 
-4 
-76 
38 
Precuneus 
 
 
11.08 
6 
-74 
40 
  Precuneus 
 
 
10.55 
-12 
-76 
46 
L. Middle Frontal Gyrus 
32/47/10 
7072 
10.17 
-30 
54 
14 
Inferior Frontal Gyrus 
 
 
9.89 
-46 
36 
8 
  Middle Frontal Gyrus 
 
 
9.08 
-46 
34 
18 
R. Middle Frontal Gyrus 
10/47/13 
4150 
8.00 
30 
54 
4 
  Middle Frontal Gyrus 
 
 
7.90 
32 
54 
16 
  Posterior Insula 
 
 
7.77 
44 
-14 
14 
L. Thalamus 
 
36 
6.69 
-24 
-28 
-2 
R. Thalamus 
 
36 
6.35 
22 
-26 
-2 
L. Superior Temporal Gyrus 
42/22 
75 
6.19 
-64 
-26 
6 
L. Inferior Parietal Lobule 
40 
323 
5.97 
-50 
-46 
50 
  Inferior Parietal Lobule 
 
 
5.91 
-58 
-36 
38 
R. Posterior Insula 
 
28 
5.55 
38 
-12 
-8 
R. Postcentral Gyrus 
2/1 
33 
5.54 
66 
-24 
36 
R. Middle Frontal Gyrus 
46 
39 
5.26 
46 
28 
26 
L. Caudate 
 
19 
5.25 
-12 
10 
2 
OT interoception > control task > PLC 
interoception > control task 
 
 
 
 
 
 
None 
 
 
 
 
 
 
OT interoception task > PLC 
interoception task 
 
 
 
 
 
 
None 
 
 
 
 
 
 
All with a PFWE < 0.05 correction threshold and cluster > 10 voxels at whole brain level. 
L indicates left; R indicates right. 
 
 
 


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Table S3. Main effect of face processing in Experiment 2 (MNI coordinates). 
Brain Region  
BA 
No. Voxels 
Peak t-value 
x 
y 
z 
L. Cuneus 
18/19/37/17 
6725 
18.65 
-20 
-96 
8 
Middle Occipital Gyrus 
 
 
18.20 
20 
-96 
12 
L. Superior Frontal Gyrus 
6/8/10 
4070 
11.44 
-12 
36 
52 
  Superior Frontal Gyrus 
 
 
11.21 
18 
56 
32 
  Middle Frontal Gyrus 
 
 
11.01 
-40 
28 
42 
R. Middle Frontal Gyrus 
9/45/46/47 
2466 
11.38 
54 
8 
42 
  Middle Frontal Gyrus 
 
 
10.72 
50 
38 
24 
R. Postcentral Gyrus 
40/2 
79 
7.54 
58 
-34 
52 
L. Superior Temporal Gyrus 
22 
16 
5.52 
-48 
2 
-2 
R. Superior Temporal Gyrus 
22 
98 
6.80 
52 
-32 
6 
L. Putamen 
 
134 
6.73 
-26 
-4 
6 
R. Putamen 
 
31 
6.02 
24 
2 
6 
R. Parahippocampal Gyrus 
 
21 
6.71 
26 
-12 
-16 
R. Thalamus 
 
31 
6.47 
22 
-28 
-2 
L. Inferior Parietal Lobule  
40 
16 
5.98 
-54 
-56 
44 
L. Medial Frontal Gyrus 
 
26 
5.82 
-10 
-18 
62 
L. Precentral Gyrus 
6 
13 
5.61 
-34 
-14 
48 
L. Anterior Insula 
 
114 
5.50a 
-46 
2 
-2 
R. Anterior Insula 
 
151 
6.41a 
46 
12 
2 
L. Anterior Cingulate Cortex 
 
55 
4.75a 
-4 
52 
12 
L. Anterior Cingulate Cortex 
 
13 
4.42a 
-4 
40 
26 
R. Anterior Cingulate Cortex 
 
37 
4.88a 
2 
54 
12 
L. Amygdala 
 
13 
4.97a 
-24 
-10 
-14 
R. Amygdala 
 
14 
5.39a 
26 
-8 
-16 
All with a PFWE < 0.05 correction threshold and cluster > 10 voxels at whole brain level. 
L indicates left; R indicates right. aPFWE < 0.05 small volume correction.