Participants
A total of 55 right-handed individuals diagnosed with BD (15 BD Ⅰ and 40 BD Ⅱ) were recruited from the in- and out-patients of psychiatry department, First Affiliated Hospital of Jinan University, Guangzhou, China. The patients’ age ranged from 18 to 55 years. All patients met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (known as DSM-V) criteria for BD according to the diagnostic assessment by the Structured Clinical Interview for DSM-V Patient Edition (SCID-P) put forwarded by two experienced psychiatrists (Y.J. and S.Z., with 21 and 6 years of experienced clinical psychiatry, respectively). The clinical state of BD patients was assessed by using the 24-item Hamilton Depression Rating Scale (HDRS) and the Young Mania Rating Scale (YMRS) during the 3-day period prior to the imaging session. The exclusion criteria included patients with other Axis-I psychiatric disorders as assessed by two experienced psychiatrists (Y.J. and S.Z., with 21 and 6 years of experienced clinical psychiatry, respectively) through SCID-P, a history of electroconvulsive therapy, other neurological disorders, any history of organic brain disorder, autoimmune/immune diseases, mental retardation, pregnancy, alcohol/substance abuse, cardiovascular diseases or presence of any concurrent and major physical illnesses as reported by laboratory examination or self-reported by patients in the experienced psychiatrists’ interview and when conversing with patients. Those patients who currently took anti-inflammatory medications or immunomodulators (i.e., statins or metformin) were excluded from the study. At the time of testing, all patients were either medication-naive, or were not medicated for at least six months. In addition, 70 right-handed healthy controls (HCs) were recruited via local advertisements. They were carefully screened through a diagnostic interview, the Structured Clinical Interview for DSM-V Nonpatient Edition (SCID-NP), to rule out the presence of current or past history of psychiatric illnesses. Further exclusion criteria for HCs include history of psychiatric illnesses in first-degree relatives, current or past significant medical or neurological illnesses.
This study was approved by the Ethics Committee of First Affiliated Hospital of Jinan University, Guangzhou, China. All subjects were right-handed and signed a written informed consent form after full writing and verbal explanation of the study. Two senior clinical psychiatrists have confirmed that all subjects had the ability to participate in the examination.
MR imaging data acquisition and preprocessing
All MRI data were obtained by using a GE Discovery MR 750 3.0T System with an 8-channel phased array head coil. Subjects were scanned in a supine, head-first position with symmetrically placed cushions on both sides of the head to decrease motion. During the scanning, participants were instructed to relax with their eyes closed without falling asleep; and after the experiment, each participant confirmed not having fallen asleep.
The rs-fMRI data were acquired using gradient-echo echo-planar imaging sequence with the following parameters: time repetition (TR)/time echo (TE) = 2000/25 ms, flip angle = 90°, voxel size = 3.75 × 3.75 × 3 mm³, field of view (FOV) = 240 × 240 mm, matrix = 64 × 64, slice thickness/gap = 3.0/1.0 mm, 35 axial slices covering the whole-brain, and 210 volumes acquired in 7 minutes. In addition, a three dimensional brain volume imaging (3D-BRAVO) sequence covering the whole brain was used for structural data acquisition with: TR/TE = 8.2/3.2 ms, flip angle = 12°, bandwidth = 31.25 Hz, slice thickness/gap = 1.0/0 mm, matrix = 256 × 256, FOV = 240 × 240 mm, NEX = 1, and acquisition time = 3 min 45 s. Routine MRI examination images were also collected for excluding anatomic abnormality. All participants were found by two experienced neuroradiologists to confirm the absence of any brain structural abnormalities.
Functional Image Preprocessing
The preprocessing was carried out using the Data Processing Assistant for Resting-State fMRI (DPABI_V3.0, http://restfmri.net/forum/DPABI) [38], and this is based on Statistical Parametric Mapping (SPM12, http://www.fil.ion.ucl.ac.uk/spm/). For each subject, the first 10 images of the rs-fMRI dataset were discarded to ensure steady-state longitudinal magnetization. The remaining 200 images were slice-time corrected initially and then were realigned to the first image for correcting the inter-TR head motion. This realignment correction provides a record of the head motion within the rs-fMRI scan. All subjects should not have more than 2 mm maximum displacement in any plane, 2° of angular motion as well as 0.2 mm in the mean frame-wise displacement (FD) [39]. The individual T1 structural images were segmented (white matter, gray matter, and cerebrospinal fluid) using a segmentation toolbox. The DARTEL toolbox was then used to create a study specific template for accurate normalization. The resting-state functional images were co-registered with the structural images and transformed into the standard Montreal Neurological Institute (MNI) space, resliced to a voxel size of 3 × 3 × 3 mm³ resolution and smoothened using a 6 mm full width at half maximum (FWHM) Gaussian kernel. The data were removed the linear trend and passed through the band-pass filter of 0.01–0.1 Hz. The global mean signal regression is commonly used in the field of rs-fMRI, and it could afford decreased dependencies on head motion [40] and increased tissue sensitivity [41]. The signals from white matter and cerebrospinal fluid were regressed out to reduce both respiratory and cardiac effects [42]. The whole brain functional connectivity estimates were influenced by head motion and the head motion could be misinterpreted as neuronal effects easily [43]. Several spurious covariates and their temporal derivatives were then regressed out from the time course of each voxel, including the signals of the brain global mean, white matter, and cerebrospinal fluid as well as the Friston-24 parameters of head motion (including the 6 head motion parameters, 6 head motion parameters at one time point before, and the 12 corresponding squared items) [44].
Resting-state Functional Connectivity
Consistent with the previous studies of amygdala FC [14], four seed masks (the bilateral lateral amygdala and medial amygdala) were selected using Brainnetome atlas (http://atlas.brainnetome.org/bnatlas.php) [45]. The time course of the masks was correlated against all other voxels within the whole brain. Individual rs-FC maps of the lateral amygdala, and medial amygdala were generated by calculating the Pearson’s correlation coefficients between the mean time series of the masks and the time series of each voxel in the whole brain. The subject-level correlation maps were then converted to the z-value maps using Fisher’s transformation to improve the normality. For all the subjects, four z-score maps that represent the intrinsic FC of the four amygdala masks were finally obtained.
Pro-inflammatory Cytokines Measures
Blood samples from BD patients and HCs were obtained in the morning under fasting condition, followed by abstaining by alcoholic beverages for at least one day prior to testing and then were processed (then frozen) by the technicians. Through venipuncture, four milliliters of fasting blood were drawn from each subject into a sterile vacuum tube (Becton & Dickinson 367812; Becton Dickinson, Franklin Lakes, NJ, USA), clotted for 30 min, and then centrifuged at 1,000 g for 15 min at 4 ℃. The serum was centrifuged again (3,000 rpm for 10 min at 4 ℃), and stored at -80 ℃ until use. Before testing, the serum should be melted on ice at low temperature, and the testing process was carried out at room temperature in accordance with the requirements of the kit to ensure effective combining of antigen and antibody. The levels of pro-inflammatory cytokines, including IL-6, IL-1β, and TNF-α were determined from the serum by the Bio-Plex Pro Human Cytokine Assay kit (Bio-Rad) according to the manufacturer’s directions using a Bio-Plex 200 array reader (Bio-Rad). Bio-Plex Manager Software, version 6.1 was used for data acquisition (Bio-Rad). The intra-assay coefficients of variation (CVs) were 3.58% for IL-1β, 2.97% for IL-6, and 3.66% for TNF-α. A reliable detection range was provided with the lower limit of quantitation (LLOQ) and the upper limit of quantitation (ULOQ). The detection ranges (LLOQ-ULOQ) were 0.057-1027.514 pg/ml for IL-1β, 0.11-1997.01 pg/ml for IL-6, and 0.69-13336.72 pg/ml for TNF-α. No immune variables were below the limits of assay detection.
Statistical analysis
Independent-sample t-test (normal variable) and Mann-Whitney U test (non-normal variable) were used to compare the demographic data (except gender) and levels of serum pro-inflammatory cytokines between the two groups with SPSS 19.0 software (SPSS, Chicago, IL, USA). A chi-squared test was performed to compare gender distribution. All tests were two-tailed, and p < 0.05 was considered to be statistically significant. One-sample t-test was performed on z-score maps for each mask to demonstrate within-group FC spatial distribution of each seed for BD patients and HCs, and the significant level was set at p < 0.05 (uncorrected). Then the two-sample t-test was performed to assess significant differences of the whole brain FC in each region between BD patients and HCs within the union mask of one-sample t-test results of both groups. Age, gender, years of education and the mean FD were included as nuisance covariates in the whole brain FC and cytokine group comparisons. Gaussian random field (GRF) theory was used for cluster-level multiple comparison correction (voxel p value < 0.001; cluster p value < 0.05, GRF corrected). When statistically significant group differences were observed in the brain regions and pro-inflammatory cytokines levels, Spearman correlation analysis was performed to compute the correlation between FC values and pro-inflammatory cytokine levels both in BD and HC groups. Also, Spearman correlation coefficients were calculated between the clinical variables and abnormal FC values, and abnormal pro-inflammatory cytokines levels in the BD group. These clinical variables included onset age of illness, number of episodes, duration of illness, 24-item HDRS scores and YMRS scores. Further, multiple linear regression was adopted to model the relationship between FC values and pro-inflammatory cytokines levels after adjusting for potential confounders including demographic variables, number of cigarettes, body mass index (BMI), and BD subgroup variable (“1” for BD I and “2” for BD II). The significant level was set as p < 0.05.