Exploring the Effects of Temperament on Gray Matter Volume of Frontal Cortex in Patients with Mood Disorders
Received 17 October 2020
Accepted for publication 14 December 2020
Published 22 January 2021 Volume 2021:17 Pages 183—193
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Yuping Ning
Tao Yang,1,2 Raymond W Lam,2 Jia Huang,1 Yousong Su,1 Jing Liu,2 Xiaorui Yang,1 Lu Yang,1 Na Zhu,3 Guoqing Zhao,4 Ruizhi Mao,1 Rubai Zhou,1 Weiping Xia,5 Hongmei Liu,1 Zuowei Wang,6 Jun Chen,1 Yiru Fang1,7,8
1Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada; 3Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, People’s Republic of China; 4Department of Psychology, Provincial Hospital Affiliated to Shandong University, Jinan, People’s Republic of China; 5Department of Medical Psychology, Xinhua Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, People’s Republic of China; 6Division of Mood Disorders, Shanghai Hongkou District Mental Health Center, Shanghai, People’s Republic of China; 7CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, People’s Republic of China; 8Shanghai Key Laboratory of Psychotic Disorders, Shanghai, People’s Republic of China
Correspondence: Yiru Fang
Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Room B2409, Building 2, 600 South Wan Ping Road, Shanghai 200030, People’s Republic of China
Tel +86 139 0199 7089
Email [email protected]
Clinical Research Center & Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Room B2411, Building 2, 600 South Wan Ping Road, Shanghai 200030, People’s Republic of China
Tel +86 180 1731 1373
Email [email protected]
Background: Patients with bipolar disorder (BD) and patients with major depressive disorder (MDD) have relatively specific temperament and structural abnormalities of brain regions related to emotion and cognition. However, the effects of temperament factors on the structure of frontal and temporal cortex is still unclear. The aims of this study were to explore the differences and relationships between temperament characteristics and the gray matter volume of frontal and temporal cortex in patients with BD or MDD.
Methods: T1-weighted magnetic resonance imaging (MRI) data, demographic and clinical information were obtained from 279 depressed patients (90 patients with BD, 189 patients with MDD) and 162 healthy controls (HC). Temperament was assessed with the Chinese short version of Temperament Evaluation of Memphis, Pisa and San Diego – Auto questionnaire (TEMPS-A). The Desikan-Killiany atlas was used for yielding gray matter volume by FreeSurfer 6.0 software suite. A total of 22 frontal and temporal regions were chosen as regions of interest (ROIs).
Results: Compared with patients with MDD, patients with BD had higher TEMPS-A total scores and scores on cyclothymic, irritable and hyperthymic subscales. The gray matter volume in bilateral rostral middle frontal gyrus (RMFG), left temporal pole and right superior frontal gyrus were reduced in patients with BD. Patients with MDD only had lower gray matter volume in bilateral temporal pole. In the pooled patients, there were negative associations between hyperthymia and gray matter volume in right RMFG.
Conclusion: Patients with BD and MDD had different temperament characteristics. The prominent temperament subscales in patients with BD were cyclothymia, irritable and hyperthymia. Patients with greater hyperthymia had lower gray matter volume in right frontal gyrus. Temperament may reflect an endophenotype in patients with mood disorders, especially in BD.
Keywords: bipolar disorder, major depressive disorder, temperament, gray matter volume
Bipolar disorder (BD) and major depressive disorder (MDD), as the primary mood disorders, are two of the most common mental disorders with an ever-increasing prevalence and global burden of disease in the past two decades.1 Despite the increased awareness of mental health, and updating of diagnostic criteria and treatment guidelines, it remains difficult to achieve early detection and prevention of BD and MDD. One of the main reasons lies in the ambiguity of etiology.
There are several etiological hypotheses for mood disorders based on the Bio-Psycho-Social disease model. Temperament is one of the proposed disease factors. Temperament is comprised of stable psychological traits in terms of intensity, speed, flexibility and directivity of psychological activities, which shows individual variation in the populations.2 Previous studies implied that temperament could be an endophenotype for mood disorders.3,4 Yuan and colleagues’ study5 suggested patients with BD were more likely to be cyclothymic, depressive, hyperthymic, irritable and anxious in temperament when compared with healthy individuals. Interestingly, patients with MDD also had high cyclothymic and irritability scores.6 Patients with both BD and MDD had higher level of harm avoidance and lower level of persistence.7 However, patients with BD and patients with MDD had relatively specific temperament characteristics.7,8 Temperament could be used for differentiation between BD and MDD and between the subtypes of BD.9 The effects of temperament on clinical symptoms have also been studied extensively. Harm avoidance was positively related to the lifetime burden of depressive episodes in mood disorders.8 Patients with cyclothymic, anxious, and irritable temperaments had more complicated presentations.10 The temperament characteristics in mood disorders such as low self-directedness and high self-transcendence were associated with suicide attempts.11 Impulsivity and poor controllability could increase the risk of suicidal acts in patients with BD who had suicidal ideation.12 In addition, patients with different temperament characteristics tended to have distinguishing treatment outcomes.13,14 The above pieces of evidence indicate that temperament might be a potential indicator for early detection and outcome prediction.
Brain structural abnormality, as an aspect of neuropathology, is also associated with mood disorders. Of all the brain regions, the frontal and temporal lobes are the critical structures of affective and cognitive circuits that are implicated in the underlying pathophysiology of mood disorders.15,16 A series of large-scale magnetic resonance imaging (MRI) studies conducted by the ENIGMA consortium demonstrated that patients with BD and MDD had reduced cortical thickness in frontal and temporal areas and smaller volume of subcortical structures.17–20 The volume of prefrontal and temporal cortex was found to be decreased.21,22 Moreover, compared with patients with MDD, patients with BD had smaller gray matter volume and thinner cortical thickness in these regions.23,24 Remarkably, brain structural abnormalities were not correlated with the severity of symptoms, but were influenced by genetic factors, age, living habits, age of onset, duration of disease, number of mood episodes, psychiatric medications and even body mass index.17,18,25,26 These might imply that the change of brain structure is a long-term process that started well before the onset of disease. Therefore, the effects of temperament on the brain development should not be ignored.
Numerous neuroimaging studies in healthy populations have revealed associations between temperament and brain structure and function. Schilling and colleagues’ study27 suggested that impulsiveness was related to the gray matter volume in the left orbitofrontal cortex among adolescence. Cyclothymic, hyperthymic and anxious temperaments were correlated with white matter properties in a wide brain area, including bilateral frontal and temporal regions.28 Healthy individuals with significant cyclothymic temperament had higher glucose metabolism in the right superior parietal gyrus.29 The ascending scores in self-directedness predicted higher brain serotonin transporter binding potential in the raphe nucleus.30 In the aspect of integrative brain function, cyclothymic and depressive temperaments had opposite relationships with the variability of intrinsic neuro-networks, especially the sensorimotor network.31 These studies consistently show that temperament affects brain structure and function.
The associations between structural and functional brain changes and temperament were detected in relatives of patients with BD.32,33 Previous studies have also suggested that temperament could affect the brain function of patients with MDD.34,35 However, the effects of temperament on the morphology of frontal and temporal cortex have not yet been understood fully. For this reason, we conducted this study to examine both temperament and brain structure in patients with mood disorders. We hypothesized that the brain volumes of frontal and temporal gyrus and the temperament characteristics in patients with BD are different from those in patients with MDD and healthy control subjects (HCs). Moreover, we hypothesized that temperament characteristics are associated with gray matter volume in these different regions.
The participants included in our study were from two clinical trials (http://www.clinicaltrials.gov; Clinical Trial Registry ID: NCT01938859 and NCT01764867), which were conducted in the Division of Mood Disorders, Shanghai Mental Health Center (SMHC), Shanghai Jiao Tong University from July 2013 to June 2017. The studies were approved by the Institutional Review Board of the SMHC. All participants signed a written informed consent at enrolment to comply with the Declaration of Helsinki.
In this study, enrolled patients with BD or MDD were right-handed Han Chinese with education levels of junior high school or above, and between 18–45 years old. They were screened by well-trained psychiatric residents using the Chinese version of the MINI-International Neuropsychiatric Interview (M.I.N.I.) in the out-patient clinic of SMHC. The diagnosis was made by two senior doctors based on the Diagnostic and Statistical Manual for Mental Disorders-Fourth Edition-Text Revision (DSM-IV-TR). All participants were required to be in a current depressive episode with Hamilton Depression Rating Scale-17 items (HAMD) total score ≥ 17, score of HAMD item-1 ≥ 2 and total score of Young Mania Rating Scale (YMRS) ≥ 10. However, patients with rapid cycling, current mixed affective episode, other psychiatric comorbidity, substance and alcohol abuse, history of neuromodulation treatment (e.g. electroconvulsive therapy) within 6 months or a history of brain trauma or severe physical condition were excluded. The exclusion criteria also included any contraindication for MRI (e.g. metal implant or claustrophobia), pregnancy or planning for pregnancy or lactating.
Healthy control participants matched with patients in age, sex, ethnicity and education were recruited from local community and college through posted advertisements. All eligible HCs had no history of mental illness, with HAMD total score < 7 and YMRS total score < 5, no history of substance use, head trauma or other significant medical conditions, no family history of major psychiatric disorders, and no contraindication for MRI.
Clinical Evaluation Instruments and Assessment
Demographic information and clinical characteristics of all participants were collected. The severity of current symptoms was assessed using HAMD, Hamilton Anxiety Rating Scale (HAMA) and YMRS. The Chinese short version of Temperament Evaluation of Memphis, Pisa and San Diego - Auto questionnaire (TEMPS-A) was used to evaluate the temperament characteristics. The short version of TEMPS-A is a self-reported, yes-or-no type of questionnaire with 39 items that generate 5 temperament subscales including cyclothymic, depressive, irritable, hyperthymic and anxious.36 The Chinese version has excellent reliability (Cronbach α = 0.92, test-retest correlation coefficient = 0.90) and validity.5
Neuroimaging Acquisition and Data Processing
All participants were scanned on the same Siemens Magnetom Verio 3T scanner (Erlangen, Germany) at the Radiological Department of SMHC after the clinical assessment. The T1-weighted magnetic resonance imaging (MRI) data were obtained using a 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence with the repetition time (TR) = 2530 ms, time to echo (TE) = 3.65 ms, inversion time (TI) = 1100 ms, field of view (FOV) = 256×256 mm, matrix = 256×256, 1 mm isotropic voxels, flip angle (FA) = 7º, bandwidth = 180 Hz/pixel, echo spacing = 8.5 ms, and in-plane acceleration factor = 2. In total, 105 patients with BD, 225 patients with MDD and 196 HCs were scanned successfully.
The FreeSurfer 6.0 software suite (http://surfer.nmr.mgh.harvard.edu/) was used to process the T1-weighted MRI data. The procedure included removing non-brain tissue, Talairach transformation, automatic segmentations of cortical and subcortical regions, intensity normalization, and automated topology correction. The Desikan-Killiany atlas was used to yield 68 gray matter volume measures and the total intracranial volume (TIV). In this study, we included the following 22 regions in frontal and temporal cortex as the regions of interest (ROIs): bilateral caudal middle frontal gyrus, inferior temporal gyrus, lateral orbitofrontal gyrus, medial orbitofrontal gyrus, middle temporal gyrus, inferior frontal gyrus (consisting of pars opercularis, pars orbitalis and pars triangularis), rostral middle frontal gyrus (RMFG), superior frontal gyrus, superior temporal gyrus, frontal pole and temporal pole.
The quality control protocol for the T1-weighted MRI dataset consisted of two steps: a visual inspection done by a trained neuroradiologist and then the ENIGMA protocol (http://enigma.ini.usc.edu/) and Qoala-T37 were used for double checking. As a result, 90 patients with BD, 189 patients with MDD and 162 HCs were enrolled.
The statistical analyses were performed with IBM SPSS Statistics 23 for Windows (Chicago Inc., USA). The Kolmogorov–Smirnov test was used to evaluate the normality of measurement data. The Kruskal–Wallis test or Mann–Whitney U-test were performed for abnormally distributed variables. The chi-square test was used for categorical variable. The post hoc multiple comparisons of demographic, clinical information and TEMPS-A scores were corrected by Bonferroni.
Regarding the gray matter volume, analysis of covariance (ANCOVA) with age, sex, education years and TIV as covariates was conducted. The multiple comparisons of all ROIs were corrected by false discovery rates (FDR).38 In addition, the effect size was evaluated by Cohen’s d.
To explore the relationships between the demographic, clinical characteristics and temperament and the gray matter volume in ROIs in pooled patients, multiple forward stepwise linear regression analyses were performed with FDR for multiple comparison correction. The gray matter volume in ROIs with significant differences between groups were chosen to be the dependent variables. The independent variables included TIV, sex, education year, age of onset, number of mood episodes, illness duration, HAMA total score, HAMD total score, YMRS total score, the scores of TEMPS-A subscales and medication history.
All the statistical analyses were two-tailed with significance threshold set at P<0.05 (Bonferroni or FDR correction).
Demographic and Clinical Characteristics
There were no differences in age and sex between groups. HCs had more years of education than patients with BD. Of the patients with BD, 31 were BD type I (BD-I), 59 were BD type II (BD-II). Compared with patients with MDD, patients with BD had earlier ages of onset, higher numbers of mood episodes and longer illness duration. Although YMRS scores were low in all groups, the BD group had higher scores than both MDD and HC groups. There were no differences between BD and MDD groups in HAMD and HAMA total score, percentage of positive family history of psychiatric disorders and percentage of never-medicated patients (Table 1). Of all the patients, only 72 (30.38%) had a history of psychiatric medication treatment. In the BD group, only 5 (6.49%) patients were on mood stabilizers, 4 (5.19%) patients were on antipsychotics, and 10 (12.99%) patients were on antidepressants. In the MDD group, 31 (19.38%) patients were on antidepressants, 7 (4.38%) patients were on antipsychotics, and 2 (1.25%) patients were on mood stabilizers.
Table 1 The Demographic Information and Clinical Characteristics of Participants
As shown in Tables 2 and S2, regardless of diagnosis and history of treatment with psychotropics, the TEMPS-A total scores and all subscale scores except the hyperthymic subscale were significantly higher in patients than those in HCs. No difference was found in the hyperthymic subscale score between patients with MDD and HCs. Compared with patients with MDD, patients with BD had higher TEMPS-A total scores and cyclothymic, irritable and hyperthymic subscale scores.
Table 2 The Differences of Temperament Between Groups (TEMPS-A)
Patients with BD-I had the same temperament characteristics. The TEMPS-A total score and the scores of all subscales in patients with BD-I were higher than those in HCs, while the TEMPS-A total score, the score of cyclothymic, irritability and hyperthymic subscales were higher than those in patients with MDD (Table S1).
In patients with BD-II, the total score of TEMPS-A, the score of cyclothymic, depressive and irritable subscales were higher than those in HCs, while TEMPS-A total score, score of cyclothymic and hyperthymic subscales were higher than those in patients with MDD (Table S1).
There were no differences in scores of TEMPS-A between patients with BD-I and patients with BD-II (Table S1).
Gray Matter Volume of Frontal and Temporal Cortex
Figure 1 illustrates the effect size of patient–control differences in gray matter volume of 22 ROIs. Compared with HCs, patients with BD had significantly lower gray matter volume in the bilateral RMFG, left temporal pole and right superior temporal gyrus (Cohen’s d range: −0.47~−0.10, FDR correction) (Figure 1A and Table S3). The gray matter volume in the left superior frontal gyrus in patients with BD tended to be lower than that in HCs (Cohen’s d = −0.13, PFDR = 0.05). The gray matter volumes in the left inferior temporal gyrus, left temporal pole and bilateral RMFG in patients with BD-I were smaller than those in HCs (Cohen’s d range: −0.48~−0.29, FDR correction); patients with BD-II only had decreased gray matter volume in the left temporal pole (Cohen’s d = −0.46, PFDR < 0.001) (Table S5). There were no differences between patients with BD-I and patients with BD-II (Cohen’s d range: −0.62~0.22). In never medicated patients, only the gray matter volume in the left temporal pole was decreased when compared with HCs (Cohen’s d = −0.50, PFDR = 0.007) (Table S6). No frontal and temporal regions with increased gray matter volume survived FDR correction.
Compared with HCs, patients with MDD only had significantly lower gray matter volume in the bilateral temporal pole (Cohen’s d = −0.37 and −0.27, PFDR < 0.01) (Figure 1B and Table S4). Patients with MDD also had slightly reduced gray matter volume in the left inferior temporal gyrus and bilateral RMFG but this did not survive FDR correction (Cohen’s d range: −0.20~-0.17). There were no frontal and temporal regions showing increased volume. Patients with MDD who were never medicated had the same changes in gray matter volume (Cohen’s d = −0.49 and −0.44, PFDR = 0.001) (Table S6).
There were no significant differences that survived FDR correction between patients with BD and patients with MDD in any gray matter volume (Cohen’s d range: −0.13~0.23), including the comparisons between BD-I and MDD (Cohen’s d range: −0.30~0.21), between BD-II and MDD (Cohen’s d range: −0.09~0.36) and between never-medicated BD and MDD (Cohen’s d range: −0.09~0.33).
Relationships Between Gray Matter Volume and Clinical Characteristics and Temperament
The relationships between gray matter volume in ROIs with significant differences and all subscale scores of TEMPS-A and clinical variables in pooled patients were explored.
For clinical characteristics, the results indicated that age of onset and illness duration were negatively associated with the gray matter volume in the bilateral RMFG and right superior temporal gyrus (PFDR < 0.05). There were no significant relationships that survived FDR correction between number of mood episodes, HAMA total score, HAMD total score, YMRS total score, history of treatment with psychotropics and gray matter volume in any ROIs (Tables S3 and S7).
Table 3 Results of Multiple Linear Regression Analysis (Forward Stepwise)
For temperament, as shown in Table 3, the TEMPS-A hyperthymic subscale score was negatively associated with gray matter volume in right RMFG. The standardized coefficient indicated that after controlling the TIV and the severity of disease (i.e. age of onset and illness duration), when the score of TEMPS-A hyperthymic subscale increased one unit, the gray matter volume in the right RMFG decreased by 16.3% unit. There were no associations between other temperament subscales and gray matter volume of ROIs (Tables S3 and S7).
In this study, we reported that patients with BD and patients with MDD had higher scores of cyclothymic, depressive, irritable, and anxious temperaments compared with healthy participants. The scores of hyperthymic temperament in patients with BD were much higher than those in patients with MDD and HCs. In addition, the scores of cyclothymic and irritable temperament were much higher in patients with BD when compared with patients with MDD. Patients with BD had reduced gray matter volume mainly in bilateral RMFG, left temporal pole and right superior temporal gyrus, while the gray matter volume in bilateral temporal pole in patients with MDD was decreased. There were no significant differences in gray matter volume in frontal and temporal cortex between patients with BD and patients with MDD. The gray matter volume of bilateral RMFG and right superior temporal gyrus were negatively associated with age of onset and illness duration. Of all temperament characteristics, hyperthymic temperament was the only one that was negatively related to the gray matter volume, in right RMFG, among all patients.
Our findings suggested that in addition to depressive and anxious temperament, both patients with BD and patients with MDD had prominent cyclothymic and irritable temperaments, which is consistent with the results of a meta-analysis.6 The instability and bipolar switching of mood is one of the typical symptoms of BD.39 Some patients with MDD also have mood swings, such as those patients who have mixed features.39 The level of cyclothymic and irritable temperaments might be related with the severity of mood instability.40 Accordingly, patients with BD (both BD-I and BD-II) had more obvious cyclothymia and irritability than patients with MDD in our study. Of note, unlike the patients with MDD and healthy individuals, we found that patients with BD-I and BD-II had higher hyperthymic temperament. This was contrary to the results of the previous meta-analysis6 which showed lower scores of hyperthymic subscale in patients with BD and patients with MDD than in healthy individuals. Our findings are more consistent with the clinical impression of BD. Even though all patients with BD in the study were in a major depressive episode, they still presented with hyperthymic temperament, suggesting that this is a stable trait marker41 for BD that is related to the excitability seen in patients with BD42 and is not changed by current mood state. Furthermore, the same pattern of temperament characteristics could be found in patients who were never medicated. Hence, hyperthymic temperament might be considered as a candidate endophenotypic difference between BD and MDD.
We included most of the important frontal and temporal regions as ROIs to detect the different brain structures which are related to cognitive and affective regulation. The results showed decreased gray matter volume in left RMFG and bilateral temporal pole in patients with BD, but only a decrease in bilateral temporal pole in patients with MDD. These changes mainly occurred in patients with BD-I. These abnormalities had been reported in previous studies as well.17,43 RMFG is one of the hubs for high-order executive functioning such as working memory, executive cognition, emotion regulation and planning.44–46 The temporal pole has been considered as an association cortex involved in cognitive and socioemotional processes, including semantic memory, empathy, emotion recognition and regulation.47–49 The cognition and emotion processes in patients with BD and MDD were found to be impaired.50 The cognitive impairments in patients with BD were worse and involved more domains than those in patients with MDD. Therefore, the brain abnormalities we found were expected. However, the effect sizes of the patient–control differences were small, which might be explained by the impact of demographic characteristics (e.g. age, sex) and clinical variables such as illness duration, number of mood episodes and psychiatric medications on brain structure.17,18 In our study, patients were generally young and had relatively short illness duration and few mood episodes (as shown in Table 1). Some patients with MDD were in their first mood episode at enrollment. About 70% of the patients were never prescribed psychotropic medications. These factors might reduce the negative effects of diseases on brain structure. For all this, we still captured the negative effects of illness duration on gray matter volume. However, the age of onset also was negatively related to the gray matter volume. This might imply that the age of onset was a nonlinear modulator for the structural brain abnormalities in patients with mood disorders.51
According to our results, patients with more prominent hyperthymic temperament had smaller gray matter volume in right RMFG. This was similar with the findings of previous studies that individuals with higher levels of impulsivity and more positive affects had thinner cortical thickness in RMFG.52,53 One underlying mechanism might be the association between temperament and coping strategy. Individuals with poor coping strategy usually are more sensitive to negative life events which can lead to chronic stress.54 Higher cortisol in people with chronic stress would then affect the morphological changes of RMFG via an oxidative stress pathway.55,56 Another potential underlying mechanism is that the temperament as a phenotype can affect the brain structure through genetic factors.57,58 In addition, serotonin transporter density in the brain was correlated with temperament which might be another mechanism to affect the brain structure.30 Again, the effect sizes of the associations between temperament and gray matter volume in RMFG in our study were small. We speculated that this is because a variety of factors besides temperament might have effects on the brain structure.25 However, because of lack of data, we were unable to perform a multivariate analysis to determine the relative contributions of temperament and other clinical factors. Relevant studies are needed in the future.
There are some limitations that should be considered. First, the cross-sectional design of this study did not allow exploration of the development of brain structural changes in patients and healthy individuals. Second, patients in this study were in a current depressive episode. The recall bias of medical history and self-rated scores of TEMPS-A might be a potential confounding factor. Third, most of our BD patients were BD-II. This may affect the interpretation of the results in patients with BD-I. Fourth, some patients had a history of psychiatric medication treatment which might affect brain structure and there may be differences in current and past medications between the BD and MDD patients. A strength of our study is that we had a large proportion of patients who were never medicated. Fifth, even though we screened the patients carefully, it is still possible that some patients with MDD may convert to a diagnosis of BD in the future. Sixth, the functional changes related to temperament in patients with mood disorders were not studied, but can be examined in future studies. Finally, because of a lack of data we could not exclude the effects of other potential confounding factors such as childhood maltreatment, lifestyles, or psychosocial adversity.
Our study provided information for understanding the associations between temperament and structure of frontal and temporal cortex in patients with mood disorders. Patients with BD and patients with MDD had different temperament characteristics. The prominent temperament domains in patients with BD were cyclothymia, irritability and hyperthymia. Patients with BD had reduced gray matter volume in frontal and temporal cortex. Patients with more prominent hyperthymia temperament had reduced gray matter volume in bilateral frontal gyrus. These findings suggest that hyperthymic temperament might be a distinct phenotype for patients with BD. Further cross-sectional and longitudinal studies in non-medicated patients with multivariate analysis are needed to explore the relative effects of temperament and other clinical factors on changes in brain structure and function.
This work was supported by the National Key R&D Program of China (2016YFC1307100), the National Natural Science Foundation of China (81761128032, 81930033, 81771465, 91232719, 81201056, 81301159), the “12th Five-year Plan” of National Key Technologies R&D program (2012BAI01B04), Shanghai Key Project of Science and Technology (2018SHZDZX05), Science and Technology Program of Guangdong (2018B030334001), the Research Program for Young Scientists of Shanghai Municipal Health Commission (20174Y0223), and also supported by the Innovative Research Team of High-level Local Universities in Shanghai. YT is supported by the China Scholarship Council (201906230123).
RWL reports personal fees from Canadian Network for Mood and Anxiety Treatments, Canadian Psychiatric Association, Hansoh, Lundbeck Institute, Pfizer, Allergan, Myriad Neuroscience, and Otsuka, outside the submitted work; and has received honoraria or research funds from Allergan, Asia-Pacific Economic Cooperation, BC Leading Edge Foundation, CIHR, CANMAT, Canadian Psychiatric Association, Hansoh, Healthy Minds Canada, Janssen, Lundbeck, Lundbeck Institute, MITACS, Ontario Brain Institute, Otsuka, Pfizer, St. Jude Medical, University Health Network Foundation, and VGH-UBCH Foundation. The authors report no other potential conflicts of interest for this work.
1. GBD. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–1858. doi:10.1016/S0140-6736(18)32279-7
2. Goldsmith HH, Buss AH, Plomin R, et al. Roundtable: what is temperament? Four approaches. Child Dev. 1987;58(2):505–529. doi:10.2307/1130527
3. Savitz JB, Ramesar RS. Personality: is it a viable endophenotype for genetic studies of bipolar affective disorder? Bipolar Disord. 2006;8(4):322–337. doi:10.1111/j.1399-5618.2006.00309.x
4. Chiaroni P, Hantouche E-G, Gouvernet J, et al. The cyclothymic temperament in healthy controls and familially at risk individuals for mood disorder: endophenotype for genetic studies? J Affect Disord. 2005;85(1–2):135–145. doi:10.1016/j.jad.2003.12.010
5. Yuan CM, Huang J, Li ZZ, et al. Reliability and validity of the Chinese short version of the Temperament Evaluation of Memphis, Pisa, Paris and San Diego-Auto Questionnaire (TEMPS-A). Chin Ment Health J. 2014;28(2):151–155.
6. Solmi M, Zaninotto L, Toffanin T, et al. A comparative meta-analysis of TEMPS scores across mood disorder patients, their first-degree relatives, healthy controls, and other psychiatric disorders. J Affect Disord. 2016;196:32–46. doi:10.1016/j.jad.2016.02.013
7. Jylha P, Mantere O, Melartin T, et al. Differences in temperament and character dimensions in patients with bipolar I or II or major depressive disorder and general population subjects. Psychol Med. 2011;41(8):1579–1591. doi:10.1017/S0033291710002606
8. Zaninotto L, Souery D, Calati R, et al. Temperament and character profiles in bipolar I, bipolar II and major depressive disorder: impact over illness course, comorbidity pattern and psychopathological features of depression. J Affect Disord. 2015;184:51–59. doi:10.1016/j.jad.2015.05.036
9. Morishita C, Kameyama R, Toda H, et al. Utility of TEMPS-A in differentiation between major depressive disorder, bipolar I disorder, and bipolar II disorder. PLoS One. 2020;15(5):e0232459. doi:10.1371/journal.pone.0232459
10. Fountoulakis KN, Gonda X, Koufaki I, et al. The role of temperament in the etiopathogenesis of bipolar spectrum illness. Harv Rev Psychiatry. 2016;24(1):36–52. doi:10.1097/HRP.0000000000000077
11. Jylha PJ, Rosenstrom T, Mantere O, et al. Temperament, character, and suicide attempts in unipolar and bipolar mood disorders. J Clin Psychiatry. 2016;77(2):252–260. doi:10.4088/JCP.14m09472
12. Jimenez E, Arias B, Mitjans M, et al. Clinical features, impulsivity, temperament and functioning and their role in suicidality in patients with bipolar disorder. Acta Psychiatr Scand. 2016;133(4):266–276. doi:10.1111/acps.12548
13. Reinares M, Pacchiarotti I, Sole B, et al. A prospective longitudinal study searching for predictors of response to group psychoeducation in bipolar disorder. J Affect Disord. 2020;274:1113–1121. doi:10.1016/j.jad.2020.02.047
14. Balestri M, Porcelli S, Souery D, et al. Temperament and character influence on depression treatment outcome. J Affect Disord. 2019;252:464–474. doi:10.1016/j.jad.2019.04.031
15. Bonelli RM, Cummings JL. Frontal-subcortical circuitry and behavior. Dialogues Clin Neurosci. 2007;9(2):141–151.
16. Price JL, Drevets WC. Neural circuits underlying the pathophysiology of mood disorders. Trends Cogn Sci. 2012;16(1):61–71. doi:10.1016/j.tics.2011.12.011
17. Hibar DP, Westlye LT, Doan NT, et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol Psychiatry. 2018;23(4):932–942. doi:10.1038/mp.2017.73
18. Schmaal L, Hibar DP, Samann PG, et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol Psychiatry. 2017;22(6):900–909. doi:10.1038/mp.2016.60
19. Hibar DP, Westlye LT, van Erp TG, et al. Subcortical volumetric abnormalities in bipolar disorder. Mol Psychiatry. 2016;21(12):1710–1716. doi:10.1038/mp.2015.227
20. Schmaal L, Veltman DJ, van Erp TG, et al. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Mol Psychiatry. 2016;21(6):806–812. doi:10.1038/mp.2015.69
21. Koolschijn PC, van Haren NE, Lensvelt-Mulders GJ, et al. Brain volume abnormalities in major depressive disorder: a meta-analysis of magnetic resonance imaging studies. Hum Brain Mapp. 2009;30(11):3719–3735. doi:10.1002/hbm.20801
22. Lu X, Zhong Y, Ma Z, et al. Structural imaging biomarkers for bipolar disorder: meta-analyses of whole-brain voxel-based morphometry studies. Depress Anxiety. 1987;58(2):353–364. doi:10.2307/1130527
23. Matsuo K, Harada K, Fujita Y, et al. Distinctive neuroanatomical substrates for depression in bipolar disorder versus major depressive disorder. Cereb Cortex. 2019;29(1):202–214. doi:10.1093/cercor/bhx319
24. Lan MJ, Chhetry BT, Oquendo MA, et al. Cortical thickness differences between bipolar depression and major depressive disorder. Bipolar Disord. 2014;16(4):378–388. doi:10.1111/bdi.12175
25. Moser DA, Doucet GE, Lee WH, et al. Multivariate associations among behavioral, clinical, and multimodal imaging phenotypes in patients with psychosis. JAMA Psychiatry. 2018;75(4):386–395. doi:10.1001/jamapsychiatry.2017.4741
26. Fears SC, Service SK, Kremeyer B, et al. Genome-wide mapping of brain phenotypes in extended pedigrees with strong genetic loading for bipolar disorder. Mol Psychiatry. 2020. doi:10.1038/s41380-020-0805-6
27. Schilling C, Kuhn S, Romanowski A, et al. Common structural correlates of trait impulsiveness and perceptual reasoning in adolescence. Hum Brain Mapp. 2013;34(2):374–383. doi:10.1002/hbm.21446
28. Hatano K, Terao T, Hayashi T, et al. Affective temperaments are associated with the white matter microstructure in healthy participants. Bipolar Disord. 2019;21(6):539–546. doi:10.1111/bdi.12726
29. Hatano K, Terao T, Hirakawa H, et al. Cyclothymic temperament and glucose metabolism in the right superior parietal lobule. Psychiatry Res Neuroimaging. 2017;270:76–79. doi:10.1016/j.pscychresns.2017.10.005
30. Tuominen L, Salo J, Hirvonen J, et al. Temperament, character and serotonin activity in the human brain: a positron emission tomography study based on a general population cohort. Psychol Med. 2013;43(4):881–894. doi:10.1017/S003329171200164X
31. Conio B, Magioncalda P, Martino M, et al. Opposing patterns of neuronal variability in the sensorimotor network mediate cyclothymic and depressive temperaments. Hum Brain Mapp. 2019;40(4):1344–1352. doi:10.1002/hbm.24453
32. Whalley HC, Sussmann JE, Chakirova G, et al. The neural basis of familial risk and temperamental variation in individuals at high risk of bipolar disorder. Biol Psychiatry. 2011;70(4):343–349. doi:10.1016/j.biopsych.2011.04.007
33. Kim E, Garrett A, Boucher S, et al. Inhibited temperament and hippocampal volume in offspring of parents with bipolar disorder. J Child Adolesc Psychopharmacol. 2017;27(3):258–265. doi:10.1089/cap.2016.0086
34. Wu H, Zheng Y, Zhan Q, et al. Covariation between spontaneous neural activity in the insula and affective temperaments is related to sleep disturbance in individuals with major depressive disorder. Psychol Med. 2019:1–10. doi:10.1017/S0033291719003647
35. Wu H, Wu C, Wu F, et al. Covariation between childhood-trauma related resting-state functional connectivity and affective temperaments is impaired in individuals with major depressive disorder. Neuroscience. 2020:. doi:10.1016/j.neuroscience.2020.08.002
36. Akiskal HS, Mendlowicz MV, Jean-Louis G, et al. TEMPS-A: validation of a short version of a self-rated instrument designed to measure variations in temperament. J Affect Disord. 2005;85(1–2):45–52. doi:10.1016/j.jad.2003.10.012
37. Klapwijk ET, van de Kamp F, van der Meulen M, et al. Qoala-T: a supervised-learning tool for quality control of FreeSurfer segmented MRI data. Neuroimage. 2019;189:116–129. doi:10.1016/j.neuroimage.2019.01.014
38. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B. 1995;57(1):289–300.
39. APA. Diagnostic and Statistical Manual of Mental Disorders (5th Edition) [M]. 2013.
40. Perugi G, Toni C, Maremmani I, et al. The influence of affective temperaments and psychopathological traits on the definition of bipolar disorder subtypes: a study on bipolar I Italian national sample. J Affect Disord. 2012;136(1–2):e41–e49. doi:10.1016/j.jad.2009.12.027
41. Neppl TK, Donnellan MB, Scaramella LV, et al. Differential stability of temperament and personality from toddlerhood to middle childhood. J Res Pers. 2010;44(3):386–396. doi:10.1016/j.jrp.2010.04.004
42. Iasevoli F, Valchera A, Di Giovambattista E, et al. Affective temperaments are associated with specific clusters of symptoms and psychopathology: a cross-sectional study on bipolar disorder inpatients in acute manic, mixed, or depressive relapse. J Affect Disord. 2013;151(2):540–550. doi:10.1016/j.jad.2013.06.041
43. Webb CA, Weber M, Mundy EA, et al. Reduced gray matter volume in the anterior cingulate, orbitofrontal cortex and thalamus as a function of mild depressive symptoms: a voxel-based morphometric analysis. Psychol Med. 2014;44(13):2833–2843. doi:10.1017/S0033291714000348
44. Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci. 2001;24(1):167–202. doi:10.1146/annurev.neuro.24.1.167
45. Phillips ML, Drevets WC, Rauch SL, et al. Neurobiology of emotion perception I: the neural basis of normal emotion perception. Biol Psychiatry. 2003;54(5):504–514. doi:10.1016/s0006-3223(03)00168-9
46. Koenigs M, Grafman J. The functional neuroanatomy of depression: distinct roles for ventromedial and dorsolateral prefrontal cortex. Behav Brain Res. 2009;201(2):239–243. doi:10.1016/j.bbr.2009.03.004
47. Nakamura K, Kawashima R, Sugiura M, et al. Neural substrates for recognition of familiar voices: a PET study. Neuropsychologia. 2001;39(10):1047–1054. doi:10.1016/s0028-3932(01)00037-9
48. Olson IR, Plotzker A, Ezzyat Y. The enigmatic temporal pole: a review of findings on social and emotional processing. Brain. 2007;130(Pt 7):1718–1731. doi:10.1093/brain/awm052
49. Blaizot X, Mansilla F, Insausti AM, et al. The human parahippocampal region: I. Temporal pole cytoarchitectonic and MRI correlation. Cereb Cortex. 2010;20(9):2198–2212. doi:10.1093/cercor/bhp289
50. Yang T, Zhao G, Mao R, et al. The association of duration and severity of disease with executive function: differences between drug-naive patients with bipolar and unipolar depression. J Affect Disord. 2018;238:412–417. doi:10.1016/j.jad.2018.05.051
51. Lampe IK, Hulshoff Pol HE, Janssen J, et al. Association of depression duration with reduction of global cerebral gray matter volume in female patients with recurrent major depressive disorder. Am J Psychiatry. 2003;160(11):2052–2054. doi:10.1176/appi.ajp.160.11.2052
52. Merz EC, He X, Noble KG, et al. Anxiety, depression, impulsivity, and brain structure in children and adolescents. Neuroimage Clin. 2018;20:243–251. doi:10.1016/j.nicl.2018.07.020
53. Michalski LJ, Demers CH, Baranger DAA, et al. Perceived stress is associated with increased rostral middle frontal gyrus cortical thickness: a family-based and discordant-sibling investigation. Genes Brain Behav. 2017;16(8):781–789. doi:10.1111/gbb.12404
54. An H, Chung S, Park J, et al. Novelty-seeking and avoidant coping strategies are associated with academic stress in Korean medical students. Psychiatry Res. 2012;200(2–3):464–468. doi:10.1016/j.psychres.2012.07.048
55. Kremen WS, O’Brien RC, Panizzon MS, et al. Salivary cortisol and prefrontal cortical thickness in middle-aged men: a twin study. Neuroimage. 2010;53(3):1093–1102. doi:10.1016/j.neuroimage.2010.02.026
56. Stomby A, Boraxbekk CJ, Lundquist A, et al. Higher diurnal salivary cortisol levels are related to smaller prefrontal cortex surface area in elderly men and women. Eur J Endocrinol. 2016;175(2):117–126. doi:10.1530/EJE-16-0352
57. Greenwood TA, Badner JA, Byerley W, et al. Heritability and genome-wide SNP linkage analysis of temperament in bipolar disorder. J Affect Disord. 2013;150(3):1031–1040. doi:10.1016/j.jad.2013.05.035
58. Bajgarova Z, Bajgar A. The relationships among MAOA, COMT Val158Met, and 5-HTTLPR polymorphisms, newborn stress reactivity, and infant temperament. Brain Behav. 2020;10(2):e01511. doi:10.1002/brb3.1511
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.