
==== Front
Transl Psychiatry
Transl Psychiatry
Translational Psychiatry
2158-3188
Nature Publishing Group UK London

3091
10.1038/s41398-024-03091-8
Article
Neural correlates of harm avoidance: a multimodal meta-analysis of brain structural and resting-state functional neuroimaging studies
Zhong Shitong 1
Lin Jinping 23
Zhang Lingsheng 1
http://orcid.org/0000-0002-4476-1915
Wang Song 4
Kemp Graham J. 5
http://orcid.org/0000-0002-0890-0279
Li Lei leili_huaxi@foxmail.com

4
http://orcid.org/0000-0002-5912-4871
Gong Qiyong qiyonggong@hmrrc.org.cn

2346
1 https://ror.org/011ashp19 grid.13291.38 0000 0001 0807 1581 West China School of Medicine, Sichuan University, Chengdu, Sichuan China
2 Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian China
3 The Xiamen Key Laboratory of Psychoradiology and Neuromodulation, Xiamen, China
4 https://ror.org/007mrxy13 grid.412901.f 0000 0004 1770 1022 Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
5 https://ror.org/04xs57h96 grid.10025.36 0000 0004 1936 8470 Liverpool Magnetic Resonance Imaging Centre and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
6 https://ror.org/02drdmm93 grid.506261.6 0000 0001 0706 7839 Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan China
20 9 2024
20 9 2024
2024
14 38412 5 2024
24 8 2024
3 9 2024
© The Author(s) 2024
2024
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Harm avoidance (HA) is a Cloninger personality trait that describes behavioural inhibition to avoid aversive stimuli. It serves as a predisposing factor that contributes to the development of mental disorders such as anxiety and major depressive disorder. Neuroimaging research has identified some brain anatomical and functional correlates of HA, but reported findings are inconsistent. We therefore conducted a multimodal meta-analysis of whole-brain structural and resting-state functional neuroimaging studies to identify the most stable neural substrate of HA. Included were a total of 10 structural voxel-based morphometry studies (11 datasets) and 13 functional positron emission tomography or single photon emission computed tomography studies (16 datasets) involving 3053 healthy participants without any psychiatric or neurological disorders evaluated for HA using the Three-Dimensional Personality Questionnaire (TPQ) or the Temperament and Character Inventory (TCI). The meta-analysis revealed brain volumetric correlates of HA in parietal and temporal cortices, and resting-state functional correlates in prefrontal, temporal and parietal gray matter. Volumetric and functional correlates co-occurred in the left superior frontal gyrus and left middle frontal gyrus, and were dissociated in the left rectus gyrus. Our meta-analysis is the first study to give a comprehensive picture of the structural and functional correlates of HA, a contribution that may help bridge the grievous gap between the neurobiology of HA and the pathogenesis, prevention and treatment of HA-related mental disorders.

Subject terms

Epigenetics and behaviour
Epigenetics in the nervous system
https://doi.org/10.13039/501100001809 National Natural Science Foundation of China (National Science Foundation of China) 82302159 82027808 Li Lei Gong Qiyong Postdoctor Research Fund of West China Hospital, Sichuan UniversityNational Key R&D Program of Chinaissue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Harm avoidance (HA) is one of Cloninger’s four dimensions of temperament, characterized by a tendency toward behavioural inhibition to avoid aversive stimuli such as punishment, loss and frustrating non-reward [1, 2]. The Three-Dimensional Personality Questionnaire (TPQ) and the Temperament and Character Inventory (TCI) are the tools most widely used to assess HA, including four subscales assessing the manifestations of anticipatory worry, fear of uncertainty, shyness with strangers and fatigability [1]. HA is associated with bias in perceptual memory and plays a vital role in personal habit formation [3]. Individuals who score high on HA tend to respond strongly to aversive stimuli, resulting in difficulty controlling their emotions [4, 5]. High HA is therefore considered a vulnerability factor for developing anxiety disorders [6], eating disorders [7], major depressive disorder [8], and obsessive-compulsive disorder [9, 10]. Exploring the neural basis of HA could be useful for helping individuals form good personal habits and reducing the risk of related mental health problems.

The relationship between HA and brain structure and function has been investigated using several neuroimaging techniques including high-resolution structural magnetic resonance imaging (sMRI), MR perfusion measurements by arterial spin labeling (ASL), positron emission tomography (PET) and single photon emission computed tomography (SPECT). Whole-brain sMRI studies have reported broad but inconsistent correlations between HA and gray matter volume (GMV) in parietal cortex [11, 12], cingulate cortex [13, 14], temporal cortex [14, 15] and prefrontal cortex [12], although four studies found no significant associations [16–19]. Resting-state functional studies measuring brain regional cerebral blood flow (rCBF), regional glucose metabolism or neuroreceptor function, have also identified HA-related alterations, but the results have been heterogeneous. For example, a SPECT study found a negative association between HA and rCBF in the right superior frontal gyrus (SFG) [20], while a PET study reported an opposite result, a positive correlation between HA and rCBF in the bilateral SFG [21]; another SPECT study reported no significant HA-related rCBF changes [22]. These inconsistencies may be due to differences in the modalities and measures or statistical analysis methods. This is something which our meta-analysis can help with, by integrating neuroimaging studies to identify robust and consistent HA-related neurophysiology.

Anisotropic effect-size seed-based d mapping (AES-SDM) is a statistical software for neuroimaging meta-analysis that has been widely used for its ability to account for the size and sign of reported effects [23, 24]. We set out to perform an AES-SDM meta-analysis to identify consistent and replicable brain anatomical and functional correlates of HA, based on whole-brain sMRI studies using voxel-based morphometry (VBM) and functional studies measuring rCBF or metabolism using ASL, PET or SPECT. We also conducted a multimodal analysis to find the most robust regional neural correlates of HA, which integrates structural and functional approaches, providing cross-modal information that is potentially unavailable in single modalities [25]. We also conducted subgroup analyses of resting-state functional studies, to distinguish the different physiologies of the measurements.

Methods

Search strategy and article inclusion

We conducted our meta-analysis according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines [26] (Supplementary Table S1). A comprehensive search strategy was conducted in PubMed, Web of Science and Embase up to the end of February 2024 with the following terms: (1) “harm avoidance” or “HA” or “Temperament and Character Inventory” or “Three-dimensional Personality Questionnaire” or “Cloninger personality traits”; and (2) “MRI” or “magnetic resonance imaging” or “voxel-based morphometry” or “vbm” or “gray matter” or “CBF” or “cerebral blood flow” or “regional cerebral metabolic” or “brain glucose metabolism” or “ASL” or “arterial spin labeling” or “PET” or “positron emission tomography” or “SPECT” or “single photon emission computed tomography”. Additional manual searches were performed using title and reference information, and where necessary and feasible authors were contacted for additional data.

Studies were included in the meta-analysis if they met these criteria: focused on HA and assessed with TPQ or TCI; reported correlation between HA and brain structure or resting-state function at whole-brain level; studied healthy participants without neurological or psychiatric disorders; and reported Montreal Neurological Institute (MNI) or Talairach coordinates. The exclusion criteria were: not published in English; not full empirical studies (e.g. reviews, meeting abstracts, letters, case reports and meta-analysis); not whole-brain analysis (e.g. region of interest (ROI) or seed voxel-based analysis); and peak coordinates of results not reported. Two authors (S.Z. and J.L.) independently reviewed and assessed each study, reaching inclusion and exclusion by consensus.

Quality assessment and data extraction

A 10-item checklist adapted from previous meta-analyses [27] was used to assess the quality, completeness and rigour of each selected study, including the demographic and clinical characteristics of the participants, the imaging-specific methodology and the reporting of the results (Supplementary Table S2): each item was scored 1, 0.5 or 0 if the criteria were fully, partially or not met, respectively. Two authors (S.Z. and J.L.) independently reviewed and assessed each paper, reaching consensus through discussion with a third author (L.L.). The included studies were of high quality, with scores from 8.5 to 10 (mean 9.4) (Supplementary Table S3). For each included study, two authors (S.Z. and J.L.) extracted the peak coordinates of significant correlates of HA at the whole-brain level for the meta-analysis, and other basic information (sample size, mean age, female ratio, scales of HA, statistical analyses and main findings).

Individual meta-analyses of structural and functional correlations

Individual voxel-based meta-analyses of regional GMV and resting-state functional associations with HA were performed separately using AES-SDM (version 5.15, http://www.sdmproject.com). For each included study, extracted peak coordinates and corresponding t values were used to recreate an effect size map using an anisotropic non-normalized Gaussian kernel, whose voxels were next permuted to randomly generate Monte Carlo brain maps; finally, individual maps were combined using meta-analytic calculations weighted by the intra-study variance and interstudy heterogeneity. AES-SDM only provides uncorrected results, so to improve study robustness we set a relatively stringent significance threshold of uncorrected p < 0.005 and SDM-Z > 1 with a cluster size of 10 voxels, which previous studies have shown can fully control the false positive rate at a level approximating corrected p < 0.05 [23, 28, 29].

Multimodal analysis

We used multimodal meta-analysis in AES-SDM to identify regions that exhibited overlapping structural and functional relationships with HA. Note that this is a different concept from correlations between structural and functional abnormalities [25].

Meta-regression analyses

We used a random-effects general linear meta-regression in AES-SDM to explore potential effects of age and gender (proportion of females) on regional brain GMV and functional correlations with HA. The SDM value served as the dependent variable, treating structural and functional meta-analyses separately. To minimize the detection of spurious relationships and reduce false positives, we increased the stringency of the significance threshold to p < 0.0005, SDM-Z > 1, and cluster size >10 voxels, while disregarding findings in regions different from those detected in the main meta-analyses [23, 30].

Reliability and subgroup analyses

To evaluate the reliability and robustness of the findings, jackknife sensitivity analysis was performed by iteratively repeating the analyses, discarding one dataset each time to see if the findings persisted as significant [23]. Subgroup analyses can help establish consistency of results and identify methodological issues contributing to differences in this meta-analysis. We did this for functional studies, examining two methodological subgroups: the ‘metabolic’ subgroup includes glucose metabolism and various receptor measurement studies, and the ‘rCBF’ subgroup includes measurements of rCBF proper by PET, SPECT, and ASL, as in previous meta-analyses [31, 32] glucose metabolism measured by 18FDG PET also reflects the rCBF.

Heterogeneity and publication bias analyses

The inter-studies heterogeneity of individual clusters detected in the meta-analysis was examined using a random-effects model with Q statistics and tested with a permutation method (a voxel threshold p < 0.005 with SDM-Z > 1 and cluster extent of 10 voxels) [30]. Egger’s test and funnel plots were used to assess potential publication bias for each cluster in the SDM, significant publication bias being determined by p < 0.05 in Egger’s test and visibly asymmetric funnel plots [33, 34].

Results

Included studies and sample characteristics

Figure 1 shows the PRISMA flowchart of the literature search and eligibility assessment. In the final meta-analyses we included 10 structural studies (11 datasets), comprising a total of 2589 healthy participants (1201 females, 1388 males, mean age 26.0 years) and 13 functional studies (16 datasets), comprising 464 healthy participants (188 females, 276 males, mean age 40.9 years) (Supplementary Table S4). Three studies [14, 35, 36] investigated the correlation between HA scores and regional brain GMV or function in male and female participants separately, and one study [21] investigated the functional correlation with HA with different gene types: these studies were treated as 2 datasets each. Table 1 summarises the demographic and analytic characteristics of the studies included in the meta-analyses.Fig. 1 Flow chart showing study identification and exclusion.

Table 1 Demographic and analytic characteristics and the main findings of studies in the meta-analysis.

Study	N (%F)	Mean age (SD)	Modality/ analysis	Scales	Nuisance covariates	Thresholds, p (correction)	Quality score	
Structural studies	
 (Yamasue et al. [114])	183 (36)	28.7 (4.2)	sMRI/VBM	TCI	TIV	p < 0.05(FDR)	9.5	
 (Iidaka et al. [15])	56 (46)	22.3 (3.1)	sMRI/VBM	TCI	Age, sex, BDI score, GMV	p < 0.001(uncorr)	9.5	
 (Gardini et al. [12])	85 (32)	32.7 (6.5)	sMRI/VBM	TPQ	Age, sex, education	p < 0.05(FDR)	10	
 (Kaasinen et al. [19])	42 (57)	59.5 (8.9)	sMRI/VBM	TCI	Age, sex	p < 0.001(Bonferroni)	9.5	
 (Ludwig et al. [11])	355 (53)	38.8 (11.1)	sMRI/VBM	TPQ	Age, sex, TIV	p < 0.001(uncorr)	9.5	
 (Van Schuerbeek et al. [13])	68 (100)	NA	sMRI/VBM	TCI	Age, total GMV	p < 0.001(uncorr)	9.5	
 (Lei et al. [17])	50 (0)	20.1	sMRI/VBM	TCI	NA	p < 0.05(TFCE)	9.5	
 (Stam et al. [16])	104 (68)	35 (11)	sMRI/VBM	TCI	Age, sex, TIV	p < 0.05(FWE)	10	
 (Yamaguchi et al. [18])	1580 (43)	20.8 (1.7)	sMRI/VBM	TCI	Age, TIV	p < 0.05(FWE)	10	
 (Stam et al. [14])_males	33 (0)	38 (13)	sMRI/VBM	TCI	Age, TIV	p < 0.05(FWE)	10	
 (Stam et al. [14])_females	33 (100)	36 (11)	sMRI/VBM	TCI	Age, TIV	p < 0.05(FWE)	10	
Resting-state functional studies	
 (Hakamata et al. [40])	31 (32)	52.7	PET/glucose metabolism	TCI	NA	p < 0.005(uncorr)	8.5	
 (Hakamata et al. [35])_males	65 (0)	52.8 (9.2)	PET/glucose metabolism	TCI	Age, other temperament dimension scores	p < 0.001(uncorr)	9.5	
 (Hakamata et al. [35])_females	37 (100)	52.6 (9.4)	PET/glucose metabolism	TCI	Age, other temperament dimension scores	p < 0.001(uncorr)	9.5	
 (Leurquin-Sterk et al. [39])	44 (50)	40.0 (14.0)	PET/mGluR5	TCI	Age, sex	p < 0.05(FWE)	10	
 (Moresco et al. [44])	11 (27)	27.1 (3.6)	PET/5-HT2A & striatal D2 receptors	TPQ	Age	p < 0.05(uncorr)	8.5	
 (O’Gorman et al. [38])	30 (50)	28.0 (6.0)	ASL/rCBF	TCI	Age, sex	p < 0.01(FWE)	10	
 (Turner et al. [22])	20 (0)	NA	SPECT/rCBF	TCI	NA	p < 0.008(Bonferroni)	9	
 (Schreckenberger et al. [43])	23 (0)	NA	PET/opioid receptor	TCI	NA	p < 0.001(cluster-level)	9	
 (Sugiura et al. [20])	30 (57)	51.0 (6.3)	SPECT/rCBF	TCI	Global mean CBF	p < 0.005(uncorr)	9	
 (Takahashi et al. [36])_males	11 (0)	31.7 (8.1)	PET/aromatase	TCI	NA	p < 0.001(uncorr)	8.5	
 (Takahashi et al. [36])_females	10 (100)	34.7 (6.4)	PET/aromatase	TCI	NA	p < 0.001(uncorr)	8.5	
 (Tuominen et al. [42])	22 (50)	37.8 (5.0)	PET/opioid receptor	TCI	Age	p < 0.05(cluster-level)	10	
 (Van Laere et al. [41])	47 (51)	35.3 (16.3)	PET/CB1R	TCI	Age, sex	p < 0.001(uncorr)	9.5	
 (Wei et al. [21])_Val	43 (51)	31.7 (8.8)	PET/rCBF	TPQ	Age, sex	p < 0.05(FDR)	10	
 (Wei et al. [21])_Met	21 (52)	35.4 (8.3)	PET/rCBF	TPQ	Age, sex	p < 0.05(FDR)	10	
 (Youn et al. [37])	19 (32)	26.3 (9.8)	PET/glucose metabolism	TCI	NA	p < 0.005(uncorr)	8.5	
HA harm avoidance, N number of sample size, %F proportion of females, SD standard deviation, M male, F female, Val Val homozygotes genotype, Met Met carriers genotype, mGluR5 metabotropic glutamate receptor 5, CB1R type 1 cannabinoid receptor, rCBF regional cerebral blood flow, sMRI structural magnetic resonance imaging, PET positron emission tomography, SPECT single photon emission computed tomography, ASL arterial spin labeling, 5-HT2A serotonin 2A receptor, TCI temperament and character inventory, TPQ three-dimensional personality questionnaire, TIV total intracranial volume, FWE family-wise error correction, FDR false discovery rate, TFCE threshold-free cluster enhancement, BDI beck depression inventory, GMV gray matter volume, R right, L left, NA not available, uncorr uncorrected p value.

Meta-analysis results

In the structural meta-analysis HA correlated positively with regional GMV in right superior temporal gyrus (STG, BA 38, extending to the right insula), left superior parietal gyrus (SPG, BA 7 and 5), and right postcentral gyrus (PoCG, BA 3); there was also significant negative correlation with regional GMV in right hippocampus (BA 37) (Fig. 2A and Table 2). In the functional meta-analysis, HA correlated with resting-state brain function positively in left superior frontal gyrus, medial (SFGmed, BA 10), right superior frontal gyrus (SFG), and right middle frontal gyrus (MFG, BA 46), and negatively in left rectus gyrus (BA 11), left PoCG (BA 4), right posterior cingulate cortex (PCC), left middle frontal gyrus, orbital part (ORBmid) and left STG (BA 48) (Fig. 2B and Table 2). In the functional subgroup meta-analysis, the positive correlation of HA in left SFGmed, right SFG and right MFG was only found in the 10 datasets of the ‘rCBF’ subgroup [20–22, 35, 37–40]; in the 11 datasets of the ‘metabolic’ subgroup [35–37, 39–44], HA correlated negatively in left rectus gyrus (BA 11), left middle temporal gyrus (BA20), left angular gyrus (BA 39), left ORBmid and Right MFG (BA 21) (Supplementary Table S5). In the multimodal analysis, positive correlation of HA with both regional GMV and resting-state function overlapped in left SFGmed (BA 10). Negative correlation of HA with both regional GMV and resting-state function overlapped in left ORBmid (BA 46). Negative correlation of HA with resting-state function and positive correlation with regional GMV overlapped in left rectus gyrus (BA 11) (Fig. 2C and Table 2).Fig. 2 Brain regions correlated with HA in the individual and multimodal meta-analyses.

A GMV showing a positive (yellow) and negative (green) correlation and (B) resting-state function showing a positive (yellow) and negative (green) correlation with HA. Clusters are displayed at voxel-wise p < 0.005, z > 1, and cluster size >10 voxels. C Brain region with combined structural and functional correlates with HA in the multimodal analyses. The left superior frontal gyrus, medial, with conjoint increased GMV and hyperfunction, is shown in red. The left middle frontal gyrus, with conjoint decreased GMV and hypofunction, is shown in blue. The left rectus gyrus with increased GMV and hypofunction, is shown in orange.

Table 2 Brain regions significantly correlated with harm avoidance in the meta-analyses.

Brain region	MNI coordinates (x, y, z)	SDM-Z	p, uncorrected	Cluster size	
Structural meta-analysis: positive correlation with regional GMV	
 R superior temporal gyrus, BA 38, extending to R insula	56, 0, −2	1.308	0.000319958	881	
 L superior parietal gyrus, BA 7	−28, −58, 56	1.325	0.000216782	350	
 L superior parietal gyrus, BA 5	−22, −52, 72	1.326	0.000216782	154	
 R postcentral gyrus, BA 3	30, −36, 66	1.322	0.000242531	295	
Structural meta-analysis: negative correlation with regional GMV	
 R hippocampus, BA 37	34, −36, −6	−1.052	0.000283837	86	
Functional meta-analysis: positive correlation with resting-state function	
 L superior frontal gyrus, medial, BA 10	−6, 60, 16	1.105	0.000046432	226	
 R superior frontal gyrus	24, 66, −2	1.106	0.000046432	214	
 R middle frontal gyrus, BA 46	34, 52, 26	1.093	0.000139356	89	
Functional meta-analysis: negative correlation with resting-state function	
 L rectus gyrus, BA 11	−4, 50, −18	−1.252	0.000892818	266	
 L postcentral gyrus, BA 4	−54, −10, 38	−1.054	0.002828121	129	
 R posterior cingulate cortex	12, −54, 10	−1.111	0.001992047	84	
 L middle frontal gyrus, orbital part	−34, 56, −16	−1.096	0.002193332	41	
 L superior temporal gyrus, BA 48	−44, 4, −16	−1.014	0.003540337	19	
Increased GMV with hyperfunction	
 L superior frontal gyrus, medial, BA 10	−8, 60, 20	NA	NA	468	
Decreased GMV with hypofunction	
 L middle frontal gyrus, orbital part, BA 46	−40, 52, −8	NA	NA	741	
Increased GMV with hypofunction	
 L rectus gyrus, BA 11	−10, 62, −22	NA	NA	86	
Clusters in individual meta-analyses were identified at voxel-wise p < 0.005, SDM-Z > 1, and cluster size >10 voxels. BA Brodmann area, SDM seed-based d mapping, NA not available, MNI Montreal Neurological Institute, GMV gray matter volume, L left, R right.

Meta-regression analyses

The mean participant age positively modulated the correlation between HA and regional GMV in left SPG (BA 7 and 5) and right PoCG (BA 3): GMV was positively associated with HA in older participants, but this was attenuated in younger participants. The percentage of females negatively modulated the correlation between HA and regional brain function in the left rectus gyrus (BA 11): HA in the healthy female subgroup was negatively associated with resting-state function in the left rectus gyrus, but this was attenuated in the healthy male subgroup. (Supplementary Table S6 and Fig. S1). However, this should be interpreted with great caution as all four relationships are dominated by a single outlier study with high effect sizes [45].

Reliability analyses

Whole-brain jackknife sensitivity analyses confirmed the reliability and replicability of the findings. In the structural meta-analysis, the positive association of HA with regional GMV in the left SPG (BA 7 and 5) and right PoCG (BA 4) remained significant in 10/11 combinations, and with right STG (BA 38) in 9/11 combinations. The negative association of HA with regional GMV in the right hippocampus (BA 37) remained significant in 10/11 combinations. In the functional meta-analysis, the positive association of HA with function in left SFGmed (BA 10), right SFG and right MFG remained significant in 15/16 combinations and the negative association in left rectus gyrus, left PoCG, right PCC and left ORBmid remained significant in 14/16 combinations. Left STG remained significant in 12/16 combinations (Supplementary Tables S7 and 8).

Heterogeneity and publication bias analyses

In the structural meta-analysis, right STG (BA 38) and left SPG (BA 7) showed significant heterogeneity among studies. there was no significant heterogeneity in the functional meta-analysis (Supplementary Table S9). No clusters from either meta-analysis showed significant potential publication bias in Egger’s test, and the funnel plots were acceptably symmetric (Supplementary Tables S10 and 11).

Discussion

To our knowledge, this is the first whole-brain meta-analysis to provide a comprehensive picture of the structural and functional correlates of HA in healthy participants. We found that HA correlated positively with regional GMV in the right superior temporal gyrus, left superior parietal gyrus and right postcentral gyrus, and negatively in the right hippocampus, positively with brain function in the bilateral superior frontal gyrus and right middle frontal gyrus, and negatively with brain function in the left rectus gyrus, left postcentral gyrus, right posterior cingulate cortex, left middle frontal gyrus and left superior temporal gyrus; the multimodal analysis found conjoint structural and functional correlates in the left superior frontal gyrus and left middle frontal gyrus and a dissociated finding in left rectus gyrus. In the meta-regression analysis age modulated the relationship between regional GMV and HA, and gender modulated the relationship between brain function and HA, although the results are dominated by a single outlier study. We discuss the possible implications of these findings below.

Structural correlations

The structural correlations are positive in the right superior temporal gyrus (STG), left superior parietal gyrus (SPG) and right postcentral gyrus (PoCG) and negative in the right hippocampus, all regions known to be relevant either to HA per se, or to its various components and contributors such as emotion regulation, pain perception or cognition.

The first region that positively correlated with HA is the right STG extending to right insula. This region is important in emotion regulation [46], the insula in particular supporting interoception of physically and emotionally arousing stimuli such as pain [47–49]. Other neuroimaging evidence has linked emotion regulation function in this region to HA. HA correlates with insula receptor availability for μ-opioid, a neurotransmitter involved in the affective component of pain and interoception [42]; in responding to punishment and making risk-taking decisions [50], individuals with high HA and greater insula activation tend to choose non-punished outcomes, in line with the “somatic marker” hypothesis, that emotions influence the decision-making process through physiological mechanisms in the insula [51, 52]. It is tempting to relate that to our finding of HA-related GMV changes in right STG to insula.

There are also important network aspects. The right insula is a key node of the salience network (SN), involved in processing external and interoceptive stimuli, including information relevant to anxiety [53]; it modulates the interactions between SN and other large-scale networks [54]. The STG is part of the default mode network (DMN), important in regulation and modulation of cognition and behaviour [55], whose dysfunction may contribute in several mental illnesses, including anxiety and depression, which have been linked to HA [56]. Other neuroimaging evidence links these networks directly to HA. Participants with high HA tend to show stronger functional connectivity within the SN and between SN and DMN [53], and SN hyperactivity has been proposed as an endophenotype of anxiety disorders [57]. This and other evidence [58, 59] implicates increased functional interactions between the SN and other large-scale networks as contributors to HA-related emotional dysfunction in the development of anxiety disorders. We speculate that this is related to the increased GMV we found in the right STG-to-insula.

The left SPG is involved in pain perception [60], which is an elemental inducer of avoidance behaviour [61]: for example, pain and anxiety are positively associated with GMV in SPG [62], and functional SPG activation is associated with response inhibition [63]. HA is closely related to pain function and avoidance behaviour [64], and individuals with high HA are more sensitive to painful stimuli [61, 65]. Moreover, excessive avoidance of potentially harmful actions may manifest as a stable personality trait [66], resembling obsessive-compulsive disorder, which is commonly associated with HA [9, 10]. We suggest that changes of GMV in SPG are associated with pain perception and behaviour inhibition in individuals with HA.

The right PoCG is important in processing somatosensory information [67]. Activation of PoCG during aesthetic judgments is related to actions to avoid the unpleasant stimulus [68, 69]. Individuals with high HA tend to more sensitive somatosensory processing [70], experiencing overstimulation and manifesting behavioural withdrawal [71, 72]. Increased GMV in the right PoCG may be the structural underpinning for greater inhibition behaviour in individuals with high HA. Regression analysis revealed that age modulates the positive associations between HA and GMV in left SPG and right PoCG. As noted above, the left SPG is crucial for pain perception and the right PoCG is engaged in somatosensory information processing, and collaboratively associated with pain-related sensory brain regions [73]. Studies have shown greater somatosensory functional activation in PoCG and SPG during tactile stimulation in healthy older adults than in younger adults [74] and higher pain thresholds in middle-aged than young adults [75]. However, this inference must be cautious due to influence of one outlier study. Longitudinal studies of specific brain regions related to HA should be considered in future research.

The right hippocampus showed a negative correlation with HA. The hippocampus is involved in the reward system by forming the reward-related memory [76]. The severity of hippocampal damage has been linked with reduced sensitivity to reward under uncertainty [77]. Moreover, in a DTI study reduced fractional anisotropy in the hippocampus was associated with high HA scores [78]. In a functional MRI study of remitted depressive individuals, HA was negatively correlated with neural activation in the hippocampus during reward anticipation, suggesting that greater avoidance behaviour and more pessimistic views towards future events were associated with lower neural activity in the hippocampus assessing the uncertainty of probabilistic reward [79]. Another functional MRI study supported this finding that the hippocampus shows a mediating role in individuals with high HA to demonstrate greater sensitivity to familiar than novel stimuli [80]. Our results, more specifically, suggest that the hippocampus atrophy may explain why individuals who score high on HA show avoidance behaviour under uncertainty.

Functional and conjoint correlates: the prefrontal cortex

Regions broadly functionally relevant to HA and related processes are mainly located in the prefrontal cortex (PFC), including the orbitofrontal cortex (OFC) and medial PFC (mPFC). The left rectus gyrus and left middle frontal gyrus, orbital part, which showed a negative correlation with HA, are subregions of the orbitofrontal cortex (OFC) and both were replicated in the multimodal analysis. The OFC is involved in executive function [81], and dysfunction of the OFC has been implicated in several HA-related mental disorders. A significant negative correlation between 5-HT binding potential in OFC and HA in depression patients [82] suggests that hypofunction of the OFC 5-HT system may contribute to executive dysfunction in depression patients who score high on HA [83]. Several studies in Parkinson’s disease have shown a linear correlation between greater HA scores and poor executive function, including impairment in planning, manipulating of information and attention [84, 85]. This correlation is also seen in patients with obsessive-compulsive disorder, who typically exhibit high HA [9, 10], and in whom hypoactivation of the OFC relates to executive dysfunction [86]. The negative association we observed with the left rectus gyrus and left middle frontal gyrus, orbital part may underpin, or at least reflect, a general executive dysfunction common to many psychiatric disorders, for which HA could be a risk factor. Regression analysis indicated that gender difference modulates the negative correlation between HA and regional function in the left rectus gyrus. There are many reports of gender differences in regional cerebral metabolism [35] and rCBF [87, 88]: OFC activity in women during the presentation of emotional stimuli positively correlates with brain estradiol levels [89]. Nonetheless, one should be cautious about this regression finding due to outlier effects.

We also found a positive correlation between HA and regional functional activity in the medial PFC (mPFC), including the left superior frontal gyrus, the right superior frontal gyrus and right middle frontal gyrus; the first of these was replicated in the multimodal analysis, indicating that the participants with higher HA shared increased regional GMV and brain function in this area. The mPFC is involved in decision-making and emotion processing [90, 91]. It is well documented that the PFC plays an important role in emotional processes through top-down control of limbic systems [92, 93] and the mPFC is associated with fear control and perpetuate pain perception [94]. High HA was associated with fear of uncertainty and greater sensitivity to losses when making decisions [95]. A neuroimaging study explored the relationship between HA and cortical thickness in healthy Korean subjects and found that HA was positively related to cortical thickness in the mPFC, which may be due to the responsibility of the above regions for the regulation of fear response and changes the level of individuals’ fear of uncertainty, thereby affecting HA [96]. It is further elucidated by functional MRI that greater activation within the mPFC was correlated with increased uncertainty during decision-making under uncertainty [97]. The mPFC, along with the STG discussed above, is also part of the DMN. Connectivity between the right insula and mPFC, which is positively related to control initiation and control maintenance behaviour [98], is negatively related to HA [99]. Thus, the response of the mPFC to decision-making, fear process and the functional connectivity of the mPFC and the right insula of DMN may underpin the behavioural inhibition of individuals with high HA. Furthermore, the mPFC is a part of the resilience network, which is an intricate adaptive system that balances the positive and negative affect on behavioural activity [100]. Resilience could be viewed as a process of adaptation to aversive stimuli [101]. It has a strong negative correlation with HA and is supported by interactions of HA with persistence and self-directedness [102]. These temperament and character interactions could predict the risk for anxiety, schizophrenia and depression through the resilience network regulating both positive and negative affect [103–105]. Our findings support this, showing that individuals with high HA have greater functional activity in the mPFC, reflecting the ability of resilience to stress to predict vulnerability to HA-related mental disorders.

Other negative functional correlates

Function in the right posterior cingulate cortex (PCC) is negatively correlated with HA. The PCC is a key hub of the DMN and is important in self-referential processing [106, 107]. In healthy subjects, PCC activation during self-referential processing of both positive and negative pictures has been identified as a significant correlation with HA [108]. A study reported that emotion processing during aversive sensations such as pain, induced efforts to terminate self-reflection and resulted in decreased processing in the PCC [109]. Another study demonstrated that the PCC did not activate when participants closed themselves from aversive stimuli; when distancing from negative stimuli, the activity of PCC increased as the negative emotions reduced [110]. Furthermore, negative moderating effects of HA were observed on functional connectivity between the right insula and DMN including the PCC, mPFC and STG [99]. Along with this evidence, our study highlights that high HA individuals with hypoactivation of the PCC and disrupted connectivity of DMN influence self-referential processing and emotion regulation, which has implications for the underpinning neurobiology of HA.

Left PoCG and left STG are also negative functional correlates. Intriguingly, higher HA scores are associated with larger GMV (see above) in right PoCG and STG, but lower function in left PoCG and STG. We have no specific explanation for this interesting apparent discrepancy, but it may relate to hemispheric asymmetry [111]: a previous study reported that patients with reduced dopamine in the right hemisphere, but not in the left, have higher HA than healthy controls [112].

Limitations and future directions

Our analysis has several limitations. Firstly, HA interacts with other personality dimensions through the temperament network and resilience system, which are two intricate adaptive systems for personalities [102]. More specifically, the same value of HA will have different influences on people who vary in their scores on the other dimensions. Thus, it is reasonable to anticipate that our meta-analyses, which concentrate on a single personality trait at a time, will show individual and group heterogeneities resulting from the interactions of multiple complex adaptive systems [104]. Future studies could usefully consider the interactions among multiple complex adaptive systems. Secondly, like most voxel-wise meta-analyses it was constructed using published coordinates rather than raw statistical brain maps [29]. Thirdly, the variation in modalities and analysis methods no doubt contributed to heterogeneity in functional meta-analysis, motivating our subgroup analyses of rCBF and metabolic datasets. Finally, our study focused on healthy individuals to investigate the neural correlates of HA. Implications for HA-related mental disorders must be cautiously drawn, and should be the explicit subject of future neuroimaging research.

In conclusion, our meta-analysis investigated HA-related VBM and resting-state functional findings and identified HA-related core brain regions in both structural and functional respects. The most robust HA correlation is the orbital and medial part of PFC, which survived in the multimodal analysis and was implicated in decision-making. Age and gender may modify these relationships, but this certainly merits further exploration. These findings add to our understanding of individual differences in HA as well as Cloninger’s temperament theory and shed light on its neural underpinnings. Our research highlights the role HA plays in mental health problems, and advances the field of psychoradiology [113].

Supplementary information

Supplementary material

Supplementary information

The online version contains supplementary material available at 10.1038/s41398-024-03091-8.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 82302159, 82027808), the National Key R&D Program of China (2022YFC2009900) and the Postdoctor Research Fund of West China Hospital, Sichuan University (2019HXBH095). The funding organizations play no further role in study design, data collection, analysis and interpretation, and paper writing.

Author contributions

SZ and JL obtained the data and wrote the main manuscript. SZ, JL, LL, JJ, LZ, and SW analyzed the results. GK, LL, and QG critically revised the manuscript. All authors reviewed the article and approved the final version of the paper for its publication.

Data availability

The data and codes that support the findings of this study are available from the corresponding author (LL) through reasonable request.

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This was a secondary analysis that all methods were performed in accordance with the PRISMA guidelines. All data used for this analysis came from the extracted data. Each original study had received ethics approval.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Shitong Zhong, Jinping Lin.
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