Participants

Thirty-one healthy ethnic Chinese subjects (15 females, mean age = 29.13, SD = 8.03, range = 21–46 years; 16 males, mean age = 33.06, SD = 10.52, range = 20–50 years) participated in the present study. To be eligible for the study, a participant had to be a person who: 1) had lived in Chongqing, China for at least six months; 2) were right-handed; 3) were free from ear-nose-throat problem; 4) had no personal or family history of psychosis, depression, suicide, epilepsy or drug abuse. We also recorded the participants’ education level and smoking index (number of cigarettes smoked per day X length of smoking history in years). The research was approved by the ethics committee of the Institute of Psychology, Chinese Academy of Sciences and was carried out in accordance with the approved guidelines. Written informed consent was obtained from all the participants before the administration of the tests.

Olfactory measures

Olfactory function was assessed birhinally using the standardized “Sniffin’ Sticks” test battery that included three tests, namely test for olfactory sensitivity/threshold, odour discrimination and odour identification36,37. Although the Sniffin’ Sticks test was standardized in European populations, it has been used extensively in Chinese populations in previous studies, such as Yang, et al.38, Yang, et al.39, Chen, et al.40, Gu and Li41, and Zou, et al.42. The results in these studies showed that the Sniffin’ Sticks olfactory test is suitable for application in Chinese populations.

The Sniffin’ Sticks test was performed according to the methods in Hummel, et al.36. For odour presentation, the experimenter removed the cap of the pens and placed the tip of the pen approximately 1–2 cm in front of both nostrils of the participants for approximately 3 s.

In the olfactory sensitivity test, odours were presented in 16 triplets of pens, two containing an almost odourless solvent and the other containing the odour (2-Phenylethanol) at a certain dilution (16 dilutions). The participants were asked to indicate the pen with the odourant. Presentation of triplets was separated by 20 s. Sensitivity was determined using a single-staircase, triple-forced choice procedure. Two successive correct identifications or one incorrect identification triggered a reversal of the staircase, i.e., the next higher or the next lower concentration step was presented, respectively. Seven reversals had to be obtained (including the starting point), and the sensitivity was defined as the mean of the last four staircase reversals.

In the odour discrimination test, 16 triplets of pens, with two containing the same odour and the third a different one, were presented to the participants. Participants were asked to identify which of the three pens smelled differently. Triplets were presented at intervals of approximately 20 s. The test result was the sum score of correctly identified pens.

In the odour identification test, 16 common odours had to be identified from a list of four descriptors (multiple forced-choice procedure). The interval between odour presentations was approximately 20 s. The test result was the sum score of correctly identified pens.

Social network measures

Social network size was assessed by the Social Network Index43. It is a 12-item questionnaire measuring the number of people the participant saw or talked to in 12 regular types of social relationships (e.g., “How many close friends do you have? How many of these friends do you see or talk to at least once every two weeks?”). These included relationships with a spouse, parents, parents-in-law, children, other close family members, neighbours, friends, workmates, schoolmates, fellow volunteers, members of groups without religious affiliation, and religious groups. The social network size was the total number of people that the participants had regular contact with at least once every two weeks, computed by summing across the 12 social relationships.

Behavioural analysis

Pearson correlation was conducted to calculate the associations between social network size and each of the olfactory tests (identification, discrimination and threshold/sensitivity). Since olfactory function may be affected by age, gender, education level and smoking index7, we also conducted partial correlation controlling for these variables. Post-hoc power analyses were performed using G*Power 3.1 software44 to determine whether the sample size could give acceptable results. Statistical power was computed as a function of significance level α, sample size, and effect size (i.e., correlation coefficient).

MRI data acquisition

Resting state fMRI data were acquired on a 3T Siemens Trio scanner (Erlangen, Germany) at the Southwest University Imaging Centre. During the scan, participants were instructed to rest quietly with their eyes closed and not to fall asleep. We collected 200 contiguous EPI functional volumes (TR = 2500 ms; TE = 30 ms; flip angle = 90°, 40 slices, matrix = 64 × 64; FOV = 220 mm; voxel size = 3.4 × 3.4 × 3.5 mm). To aid spatial normalization and localization of functional data, a T1-weighted gradient rapid acquisition gradient echo (MPRAGE) sequence was used with the following parameters (TR = 2530 ms; TE = 2.34 ms; FOV = 256 mm; voxel size = 1 × 1 × 1 mm; matrix size = 256 × 256; flip angle = 7°, slice thickness = 1 mm).

MRI data preprocessing

Image preprocessing was performed using Data Processing Assistant for Resting-State fMRI: Advanced Edition (DPARSFA, version 2.3, http://resting-fmri.sourceforge.net/) implemented in the MATLAB 2010a (Math Works, Natick, MA, USA) platform. The first 10 volumes of each participant were discarded for stabilization of the MR signal. The remaining 190 volumes were slice-time corrected and then re-aligned to the reference slice. Structural images were co-registered to individuals’ functional images, and segmented using the New Segment and DARTEL modules45. Head movement parameters were computed by estimating the translational movement in millimeters (x, y, z) and rotational motion in degrees (pitch, roll, yaw). Several nuisance variables (i.e. six motion parameters, white matter and cerebrospinal fluid signal) were regressed out from each voxel’s time series. Following this step, all images were normalized by DARTEL, and re-sampled to 3 mm isotropic voxels. Next, the images were spatially smoothed with an isotropic 4 mm full width at half maximum (FWHM) 3D-Gaussian kernel. Finally, a band-pass filter of 0.01 to 0.08 Hz was applied to the time series of each voxel to minimize the effect of low-frequency signal drift and high frequency variations.

Functional connectivity analysis

Functional connectivity analysis was performed using a seed-region based approach46. The “seeds” were placed within the left and right amygdala as defined in the automated anatomical labeling-atlas (AAL)47. All subsequent analyses were conducted separately for each region of interest (the left or the right amygdala). For each participant, time series of the voxels within each ROI were averaged to generate reference time series, and then functional connectivity maps were produced by computing the correlation coefficients between the time series of the ROI and the other remaining voxels in the whole brain. These correlation maps were converted to Z-value maps using Fisher’s r-to-z transformation to improve the Gaussianity of their distribution. In order to generate group-level statistical maps showing how social network size and olfactory function modulate functional connectivity separately, a general linear model was constructed with olfactory performance or social network size as the predictor, and age, gender, education level and smoking index as covariates. For our hypothesis-driven focus on the function of the amygdala-OFC functional connectivity, the OFC mask, combined with the bilateral orbital superior frontal gyrus, the bilateral orbital middle frontal gyrus, the bilateral orbital inferior frontal gyrus and the bilateral medial orbital superior gyrus as defined in the AAL atlas, was used for group analysis. All analyses were corrected for multiple comparisons using the AlphaSim program (http://afni.nimh.nih.gov/pub/dist/doc/manual/AlphaSim.pdf) in the Resting-State fMRI Data Analysis Toolkit (REST) software package (http://resting-fmri.sourceforge.net). Statistical maps were created using a combined threshold of p < 0.05 and a minimum cluster size of 41 voxels, yielding a threshold of p < 0.05 AlphaSim corrected. The results were displayed using the MRIcron software (http://www.nitrc.org/projects/mricron).