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Eur J Psychotraumatol
Eur J Psychotraumatol
European Journal of Psychotraumatology
2000-8066
Taylor & Francis

39297282
2403250
10.1080/20008066.2024.2403250
Version of Record
Basic Research Article
Research Article
Atrophy in the supramarginal gyrus associated with impaired cognitive inhibition in grieving Chinese Shidu parents
Atrofia del giro supramarginal asociada con una inhibición cognitiva alterada en padres Chinos en duelo por pérdida de un hijo único (Shidu)EUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY
Y. SHI ET AL.
https://orcid.org/0000-0002-9379-5580
Shi Yuqing ab
https://orcid.org/0000-0003-3995-6360
Shi Guangyuan c
https://orcid.org/0000-0002-0714-6024
Zhao Shaokun a
Wang Bolong d
Yang Yiru e
Li He f
Zhang Junying a
Wang Jianping g
Li Xin a
O’Connor Mary-Frances h
a State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People’s Republic of China
b Department of Psychology, National University of Singapore, Singapore
c Centre for Psychological Development, Tsinghua University, Beijing, People’s Republic of China
d Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, People’s Republic of China
e School of Nursing and Rehabilitation, Shandong University, Jinan, People’s Republic of China
f Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
g Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Centre for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, People’s Republic of China
h Department of Psychology, University of Arizona, Tucson, AZ, USA
CONTACT Jianping Wang wjphh@bnu.edu.cn Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Centre for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, People’s Republic of China
Xin Li lixin99@bnu.edu.cn State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People’s Republic of China
Supplemental data for this article can be accessed online at https://doi.org/10.1080/20008066.2024.2403250.

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https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

ABSTRACT

Background: The loss of an only child, known as Shidu in China, is a profoundly distressing experience, often leading to Prolonged Grief Disorder (PGD). Despite its impact, the structural brain alterations associated with PGD, potentially influencing cognitive impairments in Shidu parents, remain understudied.

Objective: This study aims to identify brain structural abnormalities related to prolonged grief and their relation with cognitive inhibition in Shidu parents.

Methods: The study included 40 Shidu parents and 42 non-bereaved participants. Prolonged grief was evaluated using the Prolonged Grief Questionnaire (PG-13). We employed voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) to assess brain structural alterations and their correlation with cognitive inhibition, as measured by Stroop interference scores.

Results: Findings suggest that greater prolonged grief intensity correlates with reduced grey matter volume in the right amygdala and the left supramarginal gyrus (SMG). Additionally, enhanced amygdala-to-whole-brain structural connectivity showed a marginal association with prolonged grief, particularly with emotional-related symptoms. Furthermore, a decrease in SMG volume was found to mediate the relation between prolonged grief and Stroop Time Inference (TI) score, indicating an indirect effect of prolonged grief on cognitive inhibition.

Conclusions: The study provides insight into the neural correlates of prolonged grief in Shidu parents, highlighting the SMG’s role in cognitive inhibition. These findings emphasise the need for comprehensive grief interventions to address the complex cognitive and emotional challenges faced by this unique bereaved population.

HIGHLIGHTS

The Shidu parents had a delay in cognitive inhibition when performing the Stroop test, compared to the control group.

Prolonged grief intensity was linked to decreased grey matter in the right amygdala and a potential increase in amygdala-to-whole-brain structural connectivity. These volumes were associated with prolonged grief symptoms related to emotions.

A higher level of prolonged grief was also associated with reduced grey matter volume in the left supramarginal gyrus, mediating the relationship between prolonged grief and Stroop Time Inference score, which indicates cognitive inhibition.

Antecedentes: La pérdida de un hijo único, conocida como Shidu en China, es una experiencia profundamente angustiante que a menudo conduce al Trastorno de Duelo Prolongado (PGD, por sus siglas en inglés). A pesar de su impacto, las alteraciones cerebrales estructurales asociadas al PGD, que pueden influir en los deterioros cognitivos de los padres cursando Shidu, siguen siendo poco estudiadas.

Objetivo: Este estudio tiene como objetivo identificar anomalías estructurales cerebrales relacionadas con el duelo prolongado y su relación con la inhibición cognitiva en padres cursando Shidu.

Métodos: El estudio incluyó a 40 padres cursando Shidu y 42 participantes que no estaban en duelo. El duelo prolongado fue evaluado utilizando el Cuestionario de Duelo Prolongado (PG-13). Usamos morfometría basada en vóxeles (VBM por sus siglas en inglés) e imágenes con tensor de difusión (DTI por sus siglas en ingles) para evaluar las alteraciones estructurales del cerebro y su correlación con la inhibición cognitiva, medida mediante puntuaciones de interferencia de Stroop.

Resultados: Los hallazgos sugieren que una mayor intensidad del duelo prolongado se correlaciona con un menor volumen de materia gris en la amígdala derecha y el giro supramarginal izquierdo (SMG, por sus siglas en inglés). Además, una mayor conectividad estructural entre la amígdala y el cerebro en su conjunto mostró una asociación marginal con el duelo prolongado, en particular con síntomas relacionados con las emociones. Además, se encontró que una disminución del volumen del SMG mediaba la relación entre el duelo prolongado y la puntuación de la inferencia temporal de Stroop (IT), indicando un efecto indirecto del duelo prolongado sobre la inhibición cognitiva.

Conclusiones: El estudio proporciona información sobre los correlatos neuronales del duelo prolongado en los padres cursando Shidu, destacando el papel del SMG en la inhibición cognitiva. Estos hallazgos enfatizan la necesidad de intervenciones integrales para el duelo, que aborden los complejos desafíos cognitivos y emocionales que enfrenta esta especial población en duelo.

KEYWORDS

Prolonged grief disorder
Shidu parents
cognitive inhibition
voxel-based morphometry
diffusion tensor imaging
PALABRAS CLAVE

Trastorno por duelo prolongado
padres Shidu
inhibición cognitiva
morfometría basada en vóxel
imagenología con tensor de difusión
National Social Science Fund of China 10.13039/501100012456 16ZDA233 Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning CNLZD1603 Natural Science Foundation of China 10.13039/501100001809 32171085 Science and Technology Innovation 2030 Major Projects 2022ZD0211600 Ministry of Education Humanities and Social Sciences Research Youth Fund Project 21YJC190013 This work was supported by National Social Science Fund of China [grant number 16ZDA233]; Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning [grant number CNLZD1603]; Natural Science Foundation of China [grant number 32171085]; Science and Technology Innovation 2030 Major Projects [grant number 2022ZD0211600]; Ministry of Education of the People's Republic of China Humanities and Social Sciences Youth Foundation [grant number 21YJC190013].
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pmc1. Introduction

China’s one-child policy (1970s–2010s) (Hesketh et al., 2005) inadvertently increased the number of parents who lost their only child, known as Shidu parents (Wang, 2013). Such a loss is a severe life event, often leading to severe psychological consequences (Xu et al., 2022; Yin et al., 2018). Among these, Prolonged Grief Disorder (PGD) is most notable, characterised by persistent and unresolved grief, exhibiting as emotional distress, along with cognitive and behavioural dysfunction (Prigerson et al., 2021). Shidu parents are at a heightened risk of developing PGD, with a prevalence of 35.5% (Zhou et al., 2020), much higher than the 10% observed in the general bereaved population (Lundorff et al., 2017). Given the profound impact losing an only child, understanding the brain and cognitive changes in Shidu parents has critical clinical and therapeutic implications.

Neuroimaging studies have robustly shown that intense grief disrupts not only daily life, but also brain functions, particularly in the limbic cortex (Arizmendi et al., 2016; Bryant et al., 2021; Fernández-Alcántara et al., 2020). For instance, bereaved individuals showed enhanced activation in the amygdala, insula, and prefrontal areas when presented with deceased-related words (Freed et al., 2009). Additionally, altered neural network communication within the default mode network (DMN) and central executive network (CEN) has been observed in Shidu parents (Liu et al., 2015). These functional disruptions associated with prolonged grief might be underpinned by structural changes in the brain (Zimmermann et al., 2016), yet research on brain structural features is relatively limited. A study reported reductions in the total volume of both grey matter and white matter in old adults with pathological grief without identifying specific brain regions (Saavedra Pérez et al., 2015). On the other hand, a whole-brain analysis found no link between affective loss and brain grey matter volume in younger adults (Acosta et al., 2018), while another whole-brain analysis revealed a weak modulation of temporal, visual, and parietal thalamic volumes (Acosta, Jansen & Kircher, 2021), suggesting possible age-dependent differences in grief's neurological impact. Considering that many Shidu parents are facing aging, their brain and cognitive challenges from prolonged grief may be more pronounced. As structural abnormalities could elucidate the long-term effects of prolonged grief on the brain, and may corroborate and expand upon findings from functional research, we aimed to identify brain regions structurally impacted by prolonged grief using a whole-brain approach.

Meanwhile, symptoms of PGD are associated with impaired cognitive abilities (Saavedra Pérez et al., 2018), notably in cognitive inhibition during tasks involving deceased-related stimuli (Maccallum & Bryant, 2010; Schneck et al., 2018). Cognitive inhibition, the active suppression mechanisms essential for restricting the processing of stimuli that are irrelevant to the ongoing task (Kipp, 2005), is experimentally measured using the Stroop colour-word test (Bugg, 2012; Hung et al., 2018; Stroop, 1935). This mechanism helps control or suppress grief-related thoughts and emotions, and deficiencies in cognitive inhibition may lead to inadequate regulation of emotional and cognitive responses, impairing one’s ability to respond adaptively to the loss event (Jollant et al., 2011). Consequently, bereaved individuals may find it challenging to divert their attention away from loss-related reminders or to engage in activities unrelated to their grief. Previous studies has identified a link between various emotional disorders and impaired cognitive inhibition (Clausen et al., 2017; Hallion et al., 2017; Richard-Devantoy et al., 2016). In prolonged grief research, the difficulty in disengaging attention from grief-related stimuli has been observed in diverse cultural contexts, including Western (Maccallum & Bryant, 2010; O’Connor & Arizmendi, 2014) and Chinese populations (Huang, 2014), through the emotional Stroop paradigm. However, the extent to which Shidu parents experience cognitive interference in neutral, non-grief-related conditions remains an open question. As symptoms of PGD extend beyond emotional responses and can impact all aspects of daily life, it is crucial to investigate cognitive impairments without using deceased-related or emotional stimuli. Also, understanding this aspect would illuminate how Shidu parents function in a variety of contexts devoid of direct grief-related stimuli, closely mirroring everyday life scenarios.

Moreover, the cognitive inhibition abilities in bereaved Shidu parents could be underpinned by specific neurological functions. Cognitive inhibition is known to engage the dorsal frontal inhibitory system, including the dorsal anterior cingulate, dorsolateral prefrontal cortex, and parietal areas (Hung et al., 2018). Notably, these regions partially overlap with those reported in grief-related research. Neuroimaging studies have revealed altered activation in areas such as the amygdala, insula, dorsolateral prefrontal cortex and orbitofrontal cortex during emotional Stroop tests in individuals experiencing severe grief (Arizmendi et al., 2016; Freed et al., 2009). Based on these findings, we proposed a hypothesis that the brain structures impacted by prolonged grief might be associated with the cognitive inhibition capabilities of Shidu parents. Specifically, this effect might be observable in their interference scores during the Stroop colour-word test, reflecting a link between grief-induced neural alterations and cognitive inhibition processing.

In the present study, we investigated structural brain alterations related to prolonged grief in Shidu parents, using voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) to estimate brain structural alterations. Next, we explored the relation between structural abnormalities and cognitive inhibition in Shidu parents, then constructed a mediation model to further elucidate the underlying neural mechanisms associated with cognitive impairments observed in Shidu parents. Our hypothesis posited that the intensity of prolonged grief correlates with alterations in brain structure, which in turn may be adversely related to cognitive inhibition in Shidu parents.

2. Materials and methods

2.1. Participants

42 participants in the Shidu group were recruited from Beijing by advertising through grief-related private organisations, volunteer teams, and neighbourhood committees. The inclusion criteria were as follows: (1) having experienced the death of an only child, with no subsequent children or adopted children; (2) time of loss exceeding 12 months; (3) age above 49, a criterion defining Shidu parents in China based on political guidelines; (4) normal cognitive function without dementia, indicated by a score of 24 or higher on the Mini-Mental Status Examination (MMSE) (Zhang et al., 1990); (5) at least an elementary school education; (6) absence of bipolar disorder, schizophrenia, obsessive-compulsive or related disorders, confirmed using the Structured Clinical Interview for DSM-5 Research Version (SCID-5-RV) (First et al., 2015); and (7) no contraindications to MRI scanning. Following the exclusion of one participant due to excessive head movement during scanning and another due to cerebral infarction, the data of 40 Shidu participants were analysed (Mage = 63.48, SD = 4.23, 65.0% female). The range of time of loss of is between 2.25 and 35.42 years.

The control group was recruited through neighbourhood committees. This group comprised 42 participants (Mage = 62.81, SD = 4.45, 73.8% female) who met the following criteria: having only one living child and no loss of first-degree relatives (parents, siblings) within the past three years. Other criteria were aligned with those of the Shidu group (i.e. items 3–7 in the section on Shidu group). The Ethics Committee and Institutional Review Board of Beijing Normal University Imaging Center for Brain Research approved this study. Participation was voluntary, with all participants signing informed consent.

2.2. Assessments

2.2.1. Questionnaires

The intensity of prolonged grief was assessed using the Prolonged Grief Questionnaire (PG-13) (Prigerson et al., 2009) by professionally trained evaluators. The PG-13 enables scoring on a continuous scale by summing the scores of symptom items, while excluding the two items related to duration and functional impairment (Pohlkamp et al., 2018). This continuous measurement approach was primarily used in our analysis, providing a more nuanced understanding than categorical classifications (Cuthbert, 2014). Therefore, it allows for a dimensional approach, where the sum score of PG-13 reflects the extent to which the participant is impacted by symptoms of PGD, instead of categorical diagnosis of PGD. In our sample, the PG-13 had excellent internal reliability (Cronbach's alpha = 0.907). Additionally, depressive symptoms in Shidu parents were evaluated using the Patient Health Questionnaire-9 (PHQ-9) (Kroenke et al., 2001), with Cronbach's α of 0.905.

2.2.2. Cognitive inhibition task

All participants underwent individual assessments of the Stroop colour-word test by professionally trained evaluators. We used the most common version of the Stroop colour-word test, as originally proposed by Stroop (1935), which requires participants to read three tables as quickly as possible. The first two tables represent the ‘congruous condition’: one where participants read 50 colour names written in black ink in Chinese characters (W condition), and the other where they name 50 colour patches (C condition). The third table, the ‘incongruent colour-word (CW) condition’, presents colour names in mismatched ink colours (e.g. the word ‘red’ in green ink), requiring participants to name the ink colour instead of the word.

Here we computed the time inference score (TI) as (Scarpina & Tagini, 2017; Van der Elst et al., 2006): TI=CWT−[(WT+CT)/2]

where TI presents the time interference score; WT is time to complete the W condition; CT is the time to complete the C condition; and CWT is the time to complete the CW condition.

Similarly, the error inference score (EI) was calculated as: EI=CWE−[(WE+CE)/2]

where EI represents the error interference score; WE is the number of errors in the W condition; CE is the number of errors in the C condition; and CWE is the number of errors in the CW condition.

One participant whose performance in the Stroop colour-word test deviated by more than three standard deviations were excluded from the analysis related to Stroop interference score.

2.3. Image acquisition

All imaging data were collected on a Siemens Trio 3.0 Tesla scanner, housed at the Imaging Center for Brain Research, Beijing Normal University. An optimised multimodality imaging protocol was used to acquire high-resolution T1-weighted and DTI images.

2.3.1. T1-weighted imaging

We acquired High-resolution sagittal 3D MP-RAGE sequences covering the entire brain. The protocol included 176 sagittal slices, with repetition time (TR) = 1900ms, echo time (TE) = 3.44 ms, slice thickness = 1 mm, flip angle =  9°, inversion time = 900 ms, field of view (FOV) = 256 × 256 mm2, and acquisition matrix = 256 × 256.

2.3.2. Diffusion tensor imaging (DTI)

In each DTI scan, whole-brain images were captured using an echo-planar imaging sequence. The parameters were set as follows: TR = 9500 ms, TE = 92 ms, slice thickness = 2 mm with no interslice gap, 70 axial slices, acquisition matrix = 128 × 128, FOV = 256 × 256 mm2, with diffusivities measured along 30 directions using a low/high b-value of 0/1000 s/mm2.

2.4. Image processing

2.4.1. VBM processing

Grey matter image preprocessing was performed on the MATLAB (2020) platform using the Computational Anatomy Toolbox (CAT12) (http://dbm.neuro.uni-jena.de/cat12/). This involved voxel-based processing and tissue segmentation of all T1 structural images. Subsequently, images were registered to the Montreal Neurological Institute (MNI) standard space through modulated normalisation. The resulting normalised grey matter images were then smoothed with a 6 mm full width at half maximum (FWHM) Gaussian kernel.

In our investigation of brain structural alterations, voxelwise correlation analysis was employed, linking prolonged grief intensity, as measured by PG-13, with grey matter volume among Shidu parents, conducted through Data Processing & Analysis of Brain Imaging (DPABI) (Yan et al., 2016). We controlled for gender, age, education, and total intracranial volume as covariates. To address multiple comparison errors in voxelwise regression, we applied Gaussian random field (GRF) theory (setting minimum Z at 2.33; cluster significance at p ≤ .01, voxel significance at p ≤ .01). Significant clusters within the cerebellum were not reported, following Liu et al. (2015), due to the relatively unclear high-level cognitive functions and the intricate involvement of the cerebellum in human cognition. Given this study's interest in cognitive performance, we focused on the cortex, which is well-recognized to play a crucial role in cognitive processing. We extracted ROI volumes from a 3 × 3 × 3 mm cube centred at the peak MNI coordinates of significant clusters. This was done to further examine the association between these structural alterations and other variables: specific PGD symptoms and Stroop performance.

2.4.2. DTI processing

DTI data preprocessing was conducted using the Pipeline for Analysing Brain Diffusion Images toolkit (PANDA) (Cui et al., 2013). The preprocessing sequence involved: (1) removing the skull from T1-weighted images and DTI images, (2) employing affine transformation to align each diffusion-weighted image with the b0 image, (3) correcting for eddy current-induced distortion and minor head motion artefacts, where DW images were registered to the b0 image via an affine transformation, and (4) tensor extraction. Subsequent deterministic fibre tracking was performed on the FA maps in individual space. Regions of interest (ROIs) were identified significant clusters derived from the VBM analysis. The structural connectivity of a cluster of interest with the entire brain (i.e. ROI-to-whole-brain structural connectivity) was calculated using the mean FA values of all fibres through this ROI. The results of deterministic fibre tracking were then overlaid on T1-weighted scans to verify whether the tracts aligned with known anatomical landmarks and expected white matter pathways to ensure they were correctly reconstructed.

2.5. Statistical analyses

Behavioural data analysis was performed in R, with mediation analysis carried out via the PROCESS tool (Hayes, 2012). Our initial step involved comparing demographic characteristics and Stroop interference scores across the Shidu and control groups. When comparing the Stroop interference scores, age (year), education (year), and gender were controlled for as covariates. We also assessed the association of prolonged grief with Stroop interference scores, while controlling gender, age and education level.

Next, we examined the relationship between prolonged grief, brain structure alterations, and Stroop interference scores among Shidu parents. For brain structural alterations, we extracted the ROI volumes indicating grey matter reduction and ROI-to-whole-brain structural connectivity, as described in the imaging processing sections. We then explored the association between prolonged grief symptoms and brain structural alterations in Shidu parents, using each symptom as a predictor and controlling for gender, age, and total intracranial volume, with False Discovery Rate (FDR) correction for p values. Furthermore, we investigated the relationship between Stroop interference scores and brain structural alterations in Shidu parents, using ROI volumes and ROI-to-whole-brain structural connectivity as predictors, controlling for gender, age and education. Finally, a mediation model within the Shidu group estimated the potential mediating role of brain abnormalities in the relationship between prolonged grief and Stroop interference, with controls for gender, age, and education.

In our analysis, depression was not included as a covariate for the consideration that PGD and major depression have overlapped symptoms (e.g. low mood), and controlling for depression might exclude important variance caused by shared symptoms, leading to an incomplete picture of the brain structural changes related to prolonged grief (Miller & Chapman, 2001; O’Connor & Arizmendi, 2014). Nevertheless, to ensure the stability of our results, an additional VBM correlation analysis was conducted, controlling for gender, education, age, depression symptoms, and time since loss.

3. Results

3.1. Demographics and Stroop colour-word test performance

A total of 82 participants were included in demographic comparison. Table 1 presents a comparison of demographic information, total intracranial volume, and Stroop interference scores between the Shidu and control groups. No significant differences were observed in demographic characteristics or total intracranial volume (p > .05). Table 1. Demography characteristics and Stroop interference scores of the participants.

 	Shidu group (n = 40)	Control group (n = 42)	 	 	
 	Mean	SD	Range	Mean	SD	Range	F/χ2	p	
Age (year)	63.475	4.230	52–71	62.810	4.446	51–71	0.481	.490	
Education (year)	10.650	2.675	5–16	10.738	2.001	5–16	0.029	.866	
Male/Female	14/26	N/A	11/31	N/A	0.392	.531	
Total intracranial volume	1367.742	128.994	1145.71–1680.90	1397.167	141.647	1041.49–1784.85	0.964	.329	
Grief-related measures	 	 	 	 	 	 	 	 	
Time since loss (year)	14.475	8.048	2–35	N/A	 	 	
PG-13	20.950	9.422	12–49	N/A	 	 	
PHQ-9	6.625	6.496	0–21	N/A	 	 	
Stroop inference scores	 	 	 	 	 	 	 	 	
TI score	47.703	16.328	13–145	39.631	15.329	9–75	4.124	.046	
EI score	1.764	2.489	−0.50–15.50	1.405	1.945	−0.50–7.00	0.409	.524	
Note: Comparison analyses of interference score were performed with age (year), education (year), and gender as covariates. Education was defined as the number of years of schooling completed. The p value of gender was obtained using a Chi-square test. TI score: time interference score; and EI score: error interference score.

Regarding Stroop interference scores, the Shidu group had significantly higher TI scores than the control group (F = 4.124, p = .046, Cohen’s d = 0.462); while the difference in EI scores was not significant. Additionally, prolonged grief intensity did not show a significantly associated with either TI (Standardised β = 0.100, SE = 0.162, t = 0.614, p = .544) or EI (Standardised β = −0.172, SE = 0.180, t = −0.955, p = .347).

3.2. Regional grey matter volume is associated with prolonged grief

As depicted in Figure 1(A), two clusters showed significant negative correlations with prolonged grief intensity: one in the right amygdala (peak voxels at x, y, z = 30, −1.5, −13.5; cluster size = 632; r = −0.52; extending to the temporal lobe and the right insula) and another in the left supramarginal gyrus (SMG; peak voxels at x, y, z = −58.5, – 34.5, 27; cluster size = 745; r = −0.58; extending to the inferior parietal lobule, IPL). Figure 1. Brain structural volume related to prolonged grief. (A) Brain regions with a significant correlation to prolonged grief intensity, highlighting the peak voxel in the right amygdala (first row) and the left SMG (second row). (B) The relationship between brain structural volume and symptoms of prolonged grief. ** adjusted p value<.01, * adjusted p value<.05.

Additional VBM correlation analysis, controlling for gender, education, age, total intracranial volume, depression symptoms, and time since loss, obtained similar results for the location of significant clusters, confirming the stability of the current findings (as detailed in Supplementary Material, Section 1). Furthermore, to demonstrate the necessity of dimensional approach, we compared the grey matter volume that showed significant associations with prolonged grief in the main analysis across three groups: high-grief Shidu parents, low-grief Shidu parents, and non-bereaved individuals. The results indicated that the intensity of prolonged grief, rather than the loss event alone, influences structural alternations in these regions. For detailed methods and results, please refer to the Supplementary Material, Section 2.

Figure 1(B) further explored the relationship between these two regions and specific prolonged grief symptoms. ROI volume of the right Amygdala was significantly associated with emotional-related symptoms, while ROI volume of the left SMG was also correlated with symptoms linked to behavioural functions, such as difficulty reengaging, numbness, and loss of meaning. These associations were found to be significant after FDR correction, with statistical details provided in Table 2. Table 2. Associations between brain structural alterations and prolonged grief symptoms

 	Stand. β	SE	t	p	Adjusted p	[95% CI of β]	
Amygdala grey matter volume	 	 	 	 	 	 	
01.Yearning	−0.504	0.147	−3.417	.002	.018	[−0.803, – 0.204]	
02. Emotional pain	−0.477	0.152	−3.130	.004	.020	[−0.786, – 0.167]	
03. Avoidance	−0.361	0.176	−2.051	.048	.088	[−0.718, – 0.003]	
04. Shocked and stunned	−0.394	0.159	−2.486	.018	.040	[−0.717, – 0.072]	
05. Role confusion	0.174	0.170	1.023	.313	.313	[−0.172, 0.519]	
06. Difficulty accepting loss	−0.413	0.159	−2.600	.014	.038	[−0.736, – 0.090]	
07. Difficulty trusting	−0.314	0.166	−1.895	.067	.103	[−0.651, 0.023]	
08. Bitterness and anger	−0.439	0.158	−2.782	.009	.032	[−0.760, – 0.118]	
09. Difficulty reengaging	−0.192	0.171	−1.119	.271	.298	[−0.540, 0.156]	
10. Numbness	−0.296	0.161	−1.837	.075	.103	[−0.623, 0.031]	
11. Loss of meaning	−0.191	0.167	−1.142	.262	.298	[−0.531, 0.149]	
SMG grey matter volume	 	 	 	 	 	 	
01.Yearning	−0.381	0.141	−2.696	.011	.020	[−0.668, – 0.094]	
02. Emotional pain	−0.477	0.135	−3.546	.001	.004	[−0.751, – 0.204]	
03. Avoidance	−0.315	0.161	−1.955	.059	.072	[−0.642, 0.012]	
04. Shocked and stunned	−0.194	0.154	−1.266	.214	.236	[−0.506, 0.118]	
05. Role confusion	−0.183	0.154	−1.188	.243	.243	[−0.496, 0.130]	
06. Difficulty accepting loss	−0.487	0.135	−3.613	<.001	.004	[−0.760, – 0.213]	
07. Difficulty trusting	−0.337	0.148	−2.278	.029	.040	[−0.637, – 0.036]	
08. Bitterness and anger	−0.392	0.144	−2.717	.010	.020	[−0.686, – 0.099]	
09. Difficulty reengaging	−0.339	0.148	−2.290	.028	.040	[−0.640, – 0.038]	
10. Numbness	−0.383	0.139	−2.752	.009	.020	[−0.666, – 0.100]	
11. Loss of meaning	−0.531	0.126	−4.218	<.001	.002	[−0.787, – 0.275]	
Amygdala-to-whole-brain FA	 	 	 	 	 	 	
01.Yearning	0.399	0.150	2.655	.012	.066	[ 0.093, 0.704]	
02. Emotional pain	0.338	0.157	2.158	.038	.140	[ 0.020, 0.657]	
03. Avoidance	0.132	0.179	0.741	.464	.567	[−0.231, 0.495]	
04. Shocked and stunned	0.280	0.160	1.753	.089	.195	[−0.045, 0.604]	
05. Role confusion	−0.185	0.164	−1.132	.266	.365	[−0.518, 0.147]	
06. Difficulty accepting loss	0.409	0.153	2.681	.011	.066	[ 0.099, 0.719]	
07. Difficulty trusting	0.310	0.160	1.941	.061	.167	[−0.015, 0.634]	
08. Bitterness and anger	0.237	0.164	1.447	.157	.288	[−0.096, 0.571]	
09. Difficulty reengaging	0.027	0.169	0.159	.875	.875	[−0.316, 0.369]	
10. Numbness	0.194	0.160	1.214	.233	.365	[−0.131, 0.519]	
11. Loss of meaning	0.085	0.164	0.517	.609	.670	[−0.249, 0.418]	
SMG-to-whole-brain FA	 	 	 	 	 	 	
01.Yearning	0.141	0.167	0.846	.404	.664	[−0.198, 0.480]	
02. Emotional pain	0.031	0.170	0.180	.858	.879	[−0.316, 0.377]	
03. Avoidance	0.248	0.179	1.389	.174	.638	[−0.115, 0.612]	
04. Shocked and stunned	0.103	0.169	0.607	.548	.753	[−0.241, 0.447]	
05. Role confusion	−0.152	0.168	−0.905	.372	.664	[−0.494, 0.190]	
06. Difficulty accepting loss	0.026	0.171	0.154	.879	.879	[−0.322, 0.375]	
07. Difficulty trusting	−0.138	0.170	−0.811	.423	.664	[−0.484, 0.208]	
08. Bitterness and anger	−0.200	0.169	−1.182	.246	.664	[−0.543, 0.144]	
09. Difficulty reengaging	0.027	0.172	0.159	.874	.879	[−0.322, 0.377]	
10. Numbness	−0.282	0.159	−1.770	.086	.638	[−0.606, 0.042]	
11. Loss of meaning	−0.248	0.163	−1.523	.137	.638	[−0.579, 0.083]	

3.3. Marginal association between amygdala structural connectivity and prolonged grief

Figure 2(A) shows the deterministic fibre tracking results and the computation of the mean FA values from white matter fibres through the two significant clusters to the whole brain. The regression analysis revealed a marginally significant relationship between prolonged grief and amygdala-to-whole-brain structural connectivity (Standardised β = 0.313, SE = 0.156, t = 2.006, p = .053). This suggests a trend of increased amygdala-to-whole-brain structural connectivity with more severe prolonged grief. Particularly, this connectivity showed a marginally association with symptoms such as yearning and difficulty accepting loss, as in Figure 2(B). Detailed statistical indices can be found in the Table 2. Meanwhile, the association between prolonged grief and SMG-to-whole-brain structural connectivity was not significant (Standardised β = 0.038, SE = 0.160, t = 0.235, p = .815). Figure 2. Brain structural connectivity measures related to prolonged grief. (A) Illustration of the fibre tracking and the mean FA calculation. (B) The relationship between structural connectivity measures in the brain and symptoms of prolonged grief. · adjusted p value<.07.

3.4. Mediating of prolonged grief’s impact on Stroop TI scores by SMG grey matter volume in Shidu parents

The linear regression model, using ROI volumes of the right amygdala and the left SMG, as well as amygdala-to-whole-brain and SMG-to-whole-brain FA as predictors, and controlling for gender, age and education level in Shidu parents, indicated a marginally prediction of TI scores by the SMG grey matter volume in Shidu parents, but not EI scores (Table 3). Table 3. The association between brain structure alterations and Stroop interference.

Predictor variable	Stand. β	SE	t	p	VIF	
TI (R2 = 0.307)	 	 	 	 	 	
ROI volume of the right amygdala	0.057	0.169	0.338	.737	1.196	
ROI volume of the left SMG	−0.356	0.183	−1.944	.062	1.404	
Amygdala-to-whole-brain FA	−0.133	0.170	−0.786	. 438	1.204	
SMG-to-whole-brain FA	0.011	0.166	0.069	.945	1.147	
Gender	−0.422	0.192	−2.200	.036	1.540	
Age	0.099	0.206	0.481	.634	1.769	
Education	−0.205	0.170	−1.208	.237	1.206	
EI (R2 = 0.073)	 	 	 	 	 	
ROI volume of the right amygdala	0.036	0.199	0.179	.859	1.197	
ROI volume of the left SMG	−0.085	0.213	−0.397	.694	1.373	
Amygdala-to-whole-brain FA	0.055	0.196	0.282	.780	1.157	
SMG-to-whole-brain FA	0.154	0.199	0.772	.446	1.202	
Gender	−0.153	0.221	−0.692	.494	1.470	
Age	0.043	0.237	0.182	.857	1.699	
Education	0.206	0.197	1.046	.304	1.175	
Note: TI score: time interference score; and EI score: error interference score.

In our mediation model within the Shidu group, which aimed to assess the potential mediating role of brain abnormalities in the relationship between prolonged grief and Stroop interference, we opted not to use the total effect as an exclusive test for mediation. Hayes (2009) pointed out that since the total effect includes both direct and all indirect effects, the indirect effects might be comparable in magnitude, and neglecting this could result in missing true mediation effects. Therefore, we used a bootstrapping approach with 5000 iterations to examine the mediation role of the left SMG volume, which appeared to correlate with both prolonged grief intensity and TI score among Shidu parents. The findings in Figure 3 revealed an indirect effect of prolonged grief on the TI scores among Shidu parents, mediated by the left SMG volume. Figure 3. Mediation effect of SMG grey matter volume. The results were estimated using a bootstrapping method with 5000 iterations, following the recommendation by Hayes (2009).

4. Discussion

After losing an only child, prolonged grief is a severe and prevalent psychological concern that profoundly impacts well-being. This study identified structural sensitivity of the prolonged grief intensity in the grey matter volume of the right amygdala and the left SMG, as well as possible increased amygdala-to-whole-brain structural connectivity. Our study observed reduced Stroop TI scores in the Shidu group compared to non-bereaved controls, potentially indicating impaired cognitive inhibition. The mediation role of the left SMG volume between prolonged grief intensity and Stroop TI scores suggests an indirect impact of prolonged grief on cognitive inhibition processes. The current study measured prolonged grief rather than acute grief reactions, given the lengthy time since the loss event. This research contributes to a deeper understanding of the neurological associations of prolonged grief, emphasising the need for comprehensive grief management strategies. For example, future studies could explore techniques such as neurofeedback training (Jirayucharoensak et al., 2019; Luijmes et al., 2016) to improve cognitive inhibition ability, with a focus on an ROI in the left SGM; cognitive–behavioural therapy and mindfulness techniques could also be further developed to enhance cognitive control for this population.

4.1. Prolonged grief is associated with the amygdala grey matter volume and its connectivity

In our study, Shidu parents with more intense prolonged grief had reduced amygdala grey matter volume and a tendency towards stronger amygdala-to-whole-brain structural connectivity. This finding of reduced right amygdala grey matter volume supports prior functional neuroimaging research that put the amygdala at the core of the grief-related neural mechanism (Bryant et al., 2021; Chen et al., 2020; Fernández-Alcántara et al., 2020; Freed et al., 2009). The amygdala’s significance in this context can be explained by its role in emotional regulation and identifying potential social dangers, including threats to attachment (Andrewes & Jenkins, 2019). Similarly, amygdala volume reductions are observed in various emotion-related mental disorders, such as first-episode major depressive disorder (Bora et al., 2012), panic disorder (Hayano et al., 2009), and post-traumatic stress disorder (Veer et al., 2015), suggesting a potential shared pathophysiological mechanism.

Our findings imply that the amygdala-to-whole-brain structural connectivity may be positively correlated with prolonged grief intensity. White matter connectivity reflects the strength of information exchange across brain regions (F. Zhang et al., 2022). This aligns with previous research showing heightened amygdala functional connectivity in prolonged grief (Chen et al., 2020), providing a structural basis for functional findings. The structural abnormalities in the amygdala predominantly related to emotional reaction symptoms in prolonged grief, consistent with its theorised role in processing emotions, regulating emotions, and detecting social threats (Andrewes & Jenkins, 2019). Nevertheless, further research is necessary to explore the marginal association observed in the present study.

4.2. Prolonged grief is related to regional grey matter volume in SMG

We observed that higher levels of prolonged grief are associated with a reduction in the grey matter volume of the left SMG. This finding aligns with previous research indicating smaller grey matter volume in the SMG following stress exposure (Hanson et al., 2010). Individuals with heightened prolonged grief may perceive higher levels of stress related to the loss, impacting the SMG more significantly. Moreover, the SMG’s importance in cognitive reappraisal, selective attention, and inhibitory control (Buhle et al., 2014; Picó-Pérez et al., 2017) suggests that its impairment could hinder effective attentional deployment. In grief regulation, a decreased SMG volume could suggest a compromised neural circuit for attention redirection from the loss, making recovery from intense grief responses more challenging.

Notably, apart from emotional reaction symptoms, SMG grey matter volume is also associated with cognitive and behavioural symptoms such as difficulty accepting loss, difficulty reengaging, numbness, and loss of meaning. This relation might be explained by the SMG's role as a critical node for sensory information integration (Blanke, 2012; Cardin & Smith, 2010), language comprehension (Hartwigsen et al., 2016), and higher cognitive processes, including social cognition (Bzdok et al., 2016), particularly in overcoming emotional egocentricity in social judgments (Silani et al., 2013). Thus, a decrease in SMG grey matter volume might be associated with diffuse functional impairment, linking it to the widespread behavioural symptoms of grief that affect various aspects of life.

Furthermore, the SMG is a vital component of the default mode network (DMN), situated at the junction of the parietal and temporal lobes. Previous grief-related studies have reported reduced distant and local centrality in the left parietal lobule (composed of the SMG and angular gyrus) among Shidu individuals (Liu et al., 2015), and resting-state functional connectivity of the SMG has been identified as a key marker in major depressive disorder (Zhu et al., 2021). These findings underscore the SMG's significance in prolonged grief and emotional disorders. However, our study did not find an association between structural connectivity of the left SMG to the whole brain and prolonged grief. The discrepancy with functional findings could be attributed to the relatively lower coupling of function and structure in the temporal and frontoparietal regions (Baum et al., 2020).

4.3. Cognitive inhibition is related to SMG grey matter volume

We observed a group difference in the Stroop TI score, indicating a potential connection to bereavement. The Stroop colour-word test, which assesses the cognitive processing of overcoming the conflict between colour and word naming, uses interference score as an indicator of cognitive inhibition (Bernal & Altman, 2009; Koss et al., 1984), with higher interference score indicates reduced cognitive inhibition ability. Since deficiencies in cognitive inhibition can result in inadequate regulation of emotional responses (Jollant et al., 2011), our findings of potentially impaired cognitive inhibition in Shidu parents compared to control group could corroborate prior research highlighting diminished attentional control in bereaved individuals, particularly towards emotion-specific stimuli (Maccallum & Bryant, 2010; O’Connor & Arizmendi, 2014). On the other hand, impaired cognitive inhibition may perpetuate symptoms in emotional disorders (Hallion et al., 2017).

Moreover, in addition to the difference associated with bereavement in the group comparison, we observed an indirect effect of prolonged grief intensity on Stroop TI scores mediated by SMG volume. This suggests that cognitive performance deficits might arise from neural structural changes, particularly in the SMG. The SMG, a critical part of the inferior parietal lobule (IPL), serves as a nexus for different brain networks, playing a crucial role in cognitive operations across various levels of neural processing hierarchy (Braga et al., 2020). Recent studies indicate the bilateral SMG's involvement in attentional reorienting and semantic processing (Numssen et al., 2021), supporting our observation that the SMG is essential for attention processing and inhibition. Thus, reduced structural integrity of the SMG might contribute to inhibitory deficits in individuals with intense prolonged grief. In line with our findings, the left inferior parietal area is one of the most frequently reported regions in the Stroop effect (Laird et al., 2005), and Loeffler et al. (2019) found the left SMG to be engaged in both Stroop and emotion regulation tasks, suggesting overlapping neural mechanisms for cognitive inhibition and emotional regulation. However, the expected significant association between the severity of prolonged grief and Stroop TI score was not observed. This might be due to the complex mechanisms underlying this relationship. Beyond the significant mediating role of SMG grey matter volume, other factors may also function as protective factors.

4.4. Limitations

The findings of this study should be viewed as exploratory rather than confirmatory, due to several limitations. Firstly, the relatively small sample size may affect reproducibility of our results. While it is challenging to recruit Shidu participants, our sample size is on par with other neuroimaging studies in grief research (Arizmendi et al., 2016; Chen et al., 2020; Fernández-Alcántara et al., 2020). The second limitation arises from the selection of participants. Our study focuses on a unique bereaved group, Shidu parents, characterised by higher grief intensity and specific cultural contexts, which may limit the generalizability of our findings to broader grieving populations. Meanwhile, although the control group was only included in the analysis comparing Stroop color-word test performance with the Shidu group, and the main analysis focused on the intensity of prolonged grief within the Shidu group, the fact that the control group did not experience any loss in the past three years does not guarantee that they are entirely non-bereaved. Thirdly, we did not collect information regarding any treatments Shidu parents may have received, which could influence the interpretation of our results. Moreover, although we chose not to control for depression and PTSD to avoid excluding important variance due to shared symptoms, the study did not examine comorbidity with depression or PTSD, which would be valuable to investigate in future research. Future studies should also consider including oppositely phase-encoded b0 images to enhance the accuracy and reliability of DTI analysis by aiding in the correction of susceptibility-induced distortions. Finally, the cross-sectional nature of our study precludes conclusions about causality. Specifically, we cannot discount the possibility that individuals with reduced grey matter volume in certain regions are inherently more prone to intense grief reactions. A longitudinal study to investigate this would be challenging due to the unpredictable nature of losing an only child. Despite these limitations, our study offers insights into the structural and cognitive impacts of prolonged grief.

5. Conclusion

In conclusion, our study shows the association of prolonged grief with both brain structure and cognitive functioning in Shidu parents. The observed alterations in the right amygdala and the left SMG not only illuminate the neural mechanisms underlying grief-related cognitive deficits but also highlight the need for comprehensive grief interventions. These findings reinforce the importance of considering the neurological aspects of grief in therapeutic approaches and underscore the need for further research to fully understand the complex interplay between grief, brain structure, and cognitive processes.

Supplementary Material

Supplementary_Material.docx

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Description of supplementary materials

The supplementary materials present the outcomes of additional VBM correlation analysis that controlled for depression symptoms and time since loss, revealing brain regions consistent with those identified in the main analyses.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.
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References

Acosta, H., Jansen, A., & Kircher, T. (2021). Larger bilateral amygdalar volumes are associated with affective loss experiences. Journal of Neuroscience Research, 99(7), 1763–1779. 10.1002/jnr.24835
Acosta, H., Jansen, A., Nuscheler, B., & Kircher, T. (2018). A voxel-based morphometry study on adult attachment style and affective loss. Neuroscience, 392 , 219–229. 10.1016/j.neuroscience.2018.06.045 30005995
Andrewes, D. G., & Jenkins, L. M. (2019). The role of the amygdala and the ventromedial prefrontal cortex in emotional regulation: Implications for post-traumatic stress disorder. Neuropsychology Review, 29 (2 ), 220–243. 10.1007/s11065-019-09398-4 30877420
Arizmendi, B., Kaszniak, A. W., & O’Connor, M.-F. (2016). Disrupted prefrontal activity during emotion processing in complicated grief: An fMRI investigation. NeuroImage, 124 , 968–976. 10.1016/j.neuroimage.2015.09.054 26434802
Baum, G. L., Cui, Z., Roalf, D. R., Ciric, R., Betzel, R. F., Larsen, B., Cieslak, M., Cook, P. A., Xia, C. H., Moore, T. M., Ruparel, K., Oathes, D. J., Alexander-Bloch, A. F., Shinohara, R. T., Raznahan, A., Gur, R. E., Gur, R. C., Bassett, D. S., & Satterthwaite, T. D. (2020). Development of structure–function coupling in human brain networks during youth. Proceedings of the National Academy of Sciences, 117 (1 ), 771–778. 10.1073/pnas.1912034117
Bernal, B., & Altman, N. (2009). Neural networks of motor and cognitive inhibition are dissociated between brain hemispheres: An fMRI study. International Journal of Neuroscience, 119 (10 ), 1848–1880. 10.1080/00207450802333029 19922390
Blanke, O. (2012). Multisensory brain mechanisms of bodily self-consciousness. Nature Reviews Neuroscience, 13 (8 ), 556–571. 10.1038/nrn3292
Bora, E., Fornito, A., Pantelis, C., & Yücel, M. (2012). Gray matter abnormalities in major depressive disorder: A meta-analysis of voxel based morphometry studies. Journal of Affective Disorders, 138 (1 ), 9–18. 10.1016/j.jad.2011.03.049 21511342
Braga, R. M., DiNicola, L. M., Becker, H. C., & Buckner, R. L. (2020). Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks. Journal of Neurophysiology, 124 (5 ), 1415–1448. 10.1152/jn.00753.2019 32965153
Bryant, R. A., Andrew, E., & Korgaonkar, M. S. (2021). Distinct neural mechanisms of emotional processing in prolonged grief disorder. Psychological Medicine, 51 (4 ), 587–595. 10.1017/S0033291719003507 31907095
Bugg, J. M. (2012). Dissociating levels of cognitive control: The case of Stroop interference. Current Directions in Psychological Science, 21 (5 ), 302–309. 10.1177/0963721412453586
Buhle, J. T., Silvers, J. A., Wager, T. D., Lopez, R., Onyemekwu, C., Kober, H., Weber, J., & Ochsner, K. N. (2014). Cognitive reappraisal of emotion: A meta-analysis of human neuroimaging studies. Cerebral Cortex (New York, NY), 24 (11 ), 2981–2990. 10.1093/cercor/bht154
Bzdok, D., Hartwigsen, G., Reid, A., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2016). Left inferior parietal lobe engagement in social cognition and language. Neuroscience & Biobehavioral Reviews, 68 , 319–334. 10.1016/j.neubiorev.2016.02.024 27241201
Cardin, V., & Smith, A. (2010). Sensitivity of human visual and vestibular cortical regions to egomotion-compatible visual stimulation. Cerebral Cortex (New York, N.Y.: 1991), 20 (8 ), 1964–1973. 10.1093/cercor/bhp268 20034998
Chen, G., Ward, B. D., Claesges, S. A., Li, S.-J., & Goveas, J. S. (2020). Amygdala functional connectivity features in grief: A pilot longitudinal study. The American Journal of Geriatric Psychiatry, 28 (10 ), 1089–1101. 10.1016/j.jagp.2020.02.014 32253102
Clausen, A. N., Francisco, A. J., Thelen, J., Bruce, J., Martin, L. E., McDowd, J., Simmons, W. K., & Aupperle, R. L. (2017). PTSD and cognitive symptoms relate to inhibition-related prefrontal activation and functional connectivity. Depression and Anxiety, 34 (5 ), 427–436. 10.1002/da.22613 28370684
Cui, Z., Zhong, S., Xu, P., He, Y., & Gong, G. (2013). PANDA: A pipeline toolbox for analyzing brain diffusion images. Frontiers in Human Neuroscience, 7 , 42. 10.3389/fnhum.2013.00042 23439846
Cuthbert, B. N. (2014). The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry, 13 (1 ), 28–35. 10.1002/wps.20087 24497240
Fernández-Alcántara, M., Verdejo-Román, J., Cruz-Quintana, F., Pérez-García, M., Catena-Martínez, A., Fernández-Ávalos, M. I., & Pérez-Marfil, M. N. (2020). Increased amygdala activations during the emotional experience of death-related pictures in complicated grief: An fMRI study. Journal of Clinical Medicine, 9 (3 ), 851. 10.3390/jcm9030851
First, M. B., Williams, J. B., Karg, R. S., & Spitzer, R. L. (2015). Structured clinical interview for DSM-5—research version (SCID-5 for DSM-5, research version; SCID-5-RV) (pp. 1–94). American Psychiatric Association.
Freed, P. J., Yanagihara, T. K., Hirsch, J., & Mann, J. J. (2009). Neural mechanisms of grief regulation. Biological Psychiatry, 66 (1 ), 33–40. 10.1016/j.biopsych.2009.01.019 19249748
Hallion, L. S., Tolin, D. F., Assaf, M., Goethe, J., & Diefenbach, G. J. (2017). Cognitive control in generalized anxiety disorder: Relation of inhibition impairments to worry and anxiety severity. Cognitive Therapy and Research, 41 (4 ), 610–618. 10.1007/s10608-017-9832-2
Hanson, J. L., Chung, M. K., Avants, B. B., Shirtcliff, E. A., Gee, J. C., Davidson, R. J., & Pollak, S. D. (2010). Early stress is associated with alterations in the orbitofrontal cortex: A tensor-based morphometry investigation of brain structure and behavioral risk. The Journal of Neuroscience, 30 (22 ), 7466–7472. 10.1523/JNEUROSCI.0859-10.2010 20519521
Hartwigsen, G., Weigel, A., Schuschan, P., Siebner, H. R., Weise, D., Classen, J., & Saur, D. (2016). Dissociating parieto-frontal networks for phonological and semantic word decisions: A condition-and-perturb TMS study. Cerebral Cortex, 26 (6 ), 2590–2601. 10.1093/cercor/bhv092 25953770
Hayano, F., Nakamura, M., Asami, T., Uehara, K., Yoshida, T., Roppongi, T., Otsuka, T., Inoue, T., & Hirayasu, Y. (2009). Smaller amygdala is associated with anxiety in patients with panic disorder. Psychiatry and Clinical Neurosciences, 63 (3 ), 266–276. 10.1111/j.1440-1819.2009.01960.x 19566756
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76 (4 ), 408–420. 10.1080/03637750903310360
Hayes, A. F. (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling. University of Kansas.
Hesketh, T., Lu, L., & Xing, Z. W. (2005). The effect of China’s one-child family policy after 25 years. New England Journal of Medicine, 353 (11 ), 1171–1176. 10.1056/NEJMhpr051833 16162890
Huang, Y. (2014). Attentional bias of bereaved parents in Wenchuan earthquake [Master's thesis]. CNKI Master's Theses Database, Southwest University.
Hung, Y., Gaillard, S. L., Yarmak, P., & Arsalidou, M. (2018). Dissociations of cognitive inhibition, response inhibition, and emotional interference: Voxelwise ALE meta-analyses of fMRI studies. Human Brain Mapping, 39 (10 ), 4065–4082. 10.1002/hbm.24232 29923271
Jirayucharoensak, S., Israsena, P., Pan-Ngum, S., Hemrungrojn, S., & Maes, M. (2019). A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment. Clinical Interventions in Aging, 14 , 347–360. 10.2147/CIA.S189047 30863028
Jollant, F., Lawrence, N. L., Olié, E., Guillaume, S., & Courtet, P. (2011). The suicidal mind and brain: A review of neuropsychological and neuroimaging studies. The World Journal of Biological Psychiatry, 12 (5 ), 319–339. 10.3109/15622975.2011.556200 21385016
Kipp, K. (2005). A developmental perspective on the measurement of cognitive deficits in attention-deficit/hyperactivity disorder. Biological Psychiatry, 57 (11 ), 1256–1260. 10.1016/j.biopsych.2005.03.012 15949996
Koss, E., Ober, B. A., Delis, D. C., & Friedland, R. P. (1984). The Stroop color-word test: Indicator of dementia severity. International Journal of Neuroscience, 24 (1 ), 53–61. 10.3109/00207458409079534 6480252
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9. Journal of General Internal Medicine, 16 (9 ), 606–613. 10.1046/j.1525-1497.2001.016009606.x 11556941
Laird, A. R., McMillan, K. M., Lancaster, J. L., Kochunov, P., Turkeltaub, P. E., Pardo, J. V., & Fox, P. T. (2005). A comparison of label-based review and ALE meta-analysis in the Stroop task. Human Brain Mapping, 25 (1 ), 6–21. 10.1002/hbm.20129 15846823
Liu, W., Liu, H., Wei, D., Sun, J., Yang, J., Meng, J., Wang, L., & Qiu, J. (2015). Abnormal degree centrality of functional hubs associated with negative coping in older Chinese adults who lost their only child. Biological Psychology, 112 , 46–55. 10.1016/j.biopsycho.2015.09.005 26391339
Loeffler, L. A. K., Satterthwaite, T. D., Habel, U., Schneider, F., Radke, S., & Derntl, B. (2019). Attention control and its emotion-specific association with cognitive emotion regulation in depression. Brain Imaging and Behavior, 13 (6 ), 1766–1779. 10.1007/s11682-019-00174-9 31414234
Luijmes, R. E., Pouwels, S., & Boonman, J. (2016). The effectiveness of neurofeedback on cognitive functioning in patients with Alzheimer’s disease: Preliminary results. Neurophysiologie Clinique/Clinical Neurophysiology, 46 (3 ), 179–187. 10.1016/j.neucli.2016.05.069 27374996
Lundorff, M., Holmgren, H., Zachariae, R., Farver-Vestergaard, I., & O’Connor, M. (2017). Prevalence of prolonged grief disorder in adult bereavement: A systematic review and meta-analysis. Journal of Affective Disorders, 212 , 138–149. 10.1016/j.jad.2017.01.030 28167398
Maccallum, F., & Bryant, R. A. (2010). Attentional bias in complicated grief. Journal of Affective Disorders, 125 (1 ), 316–322. 10.1016/j.jad.2010.01.070 20483163
Miller, G. A., & Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110 (1 ), 40–48. 10.1037/0021-843X.110.1.40 11261398
Numssen, O., Bzdok, D., & Hartwigsen, G. (2021). Functional specialization within the inferior parietal lobes across cognitive domains. eLife, 10 , e63591. 10.7554/eLife.63591 33650486
O’Connor, M.-F., & Arizmendi, B. J. (2014). Neuropsychological correlates of complicated grief in older spousally bereaved adults. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69B (1 ), 12–18. 10.1093/geronb/gbt025
Picó-Pérez, M., Radua, J., Steward, T., Menchón, J. M., & Soriano-Mas, C. (2017). Emotion regulation in mood and anxiety disorders: A meta-analysis of fMRI cognitive reappraisal studies. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 79 , 96–104. 10.1016/j.pnpbp.2017.06.001 28579400
Pohlkamp, L., Kreicbergs, U., Prigerson, H. G., & Sveen, J. (2018). Psychometric properties of the prolonged grief disorder-13 (PG-13) in bereaved Swedish parents. Psychiatry Research, 267 , 560–565. 10.1016/j.psychres.2018.06.004 29982112
Prigerson, H. G., Boelen, P. A., Xu, J., Smith, K. V., & Maciejewski, P. K. (2021). Validation of the new DSM-5-TR criteria for prolonged grief disorder and the PG-13-revised (PG-13-R) scale. World Psychiatry, 20 (1 ), 96–106. 10.1002/wps.20823 33432758
Prigerson, H. G., Horowitz, M. J., Jacobs, S. C., Parkes, C. M., Aslan, M., Goodkin, K., Raphael, B., Marwit, S. J., Wortman, C., Neimeyer, R. A., Bonanno, G. A., Bonanno, G., Block, S. D., Kissane, D., Boelen, P., Maercker, A., Litz, B. T., Johnson, J. G., First, M. B., & Maciejewski, P. K. (2009). Prolonged grief disorder: Psychometric validation of criteria proposed for DSM-V and ICD-11. PLoS Medicine, 6 (8 ), e1000121. 10.1371/journal.pmed.1000121 19652695
Richard-Devantoy, S., Ding, Y., Lepage, M., Turecki, G., & Jollant, F. (2016). Cognitive inhibition in depression and suicidal behavior: A neuroimaging study. Psychological Medicine, 46 (5 ), 933–944. 10.1017/S0033291715002421 26670261
Saavedra Pérez, H. C., Ikram, M. A., Direk, N., Prigerson, H. G., Freak-Poli, R., Verhaaren, B. F. J., Hofman, A., Vernooij, M., & Tiemeier, H. (2015). Cognition, structural brain changes and complicated grief. A population-based study. Psychological Medicine, 45 (7 ), 1389–1399. 10.1017/S0033291714002499 25363662
Saavedra Pérez, H. C., Ikram, M. A., Direk, N., & Tiemeier, H. (2018). Prolonged grief and cognitive decline: A prospective population-based study in middle-aged and older persons. The American Journal of Geriatric Psychiatry, 26 (4 ), 451–460. 10.1016/j.jagp.2017.12.003 29329723
Scarpina, F., & Tagini, S. (2017). The Stroop color and word test. Frontiers in Psychology, 8 , 557. 10.3389/fpsyg.2017.00557 28446889
Schneck, N., Tu, T., Michel, C. A., Bonanno, G. A., Sajda, P., & Mann, J. J. (2018). Attentional bias to reminders of the deceased as compared with a living attachment in grieving. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3 (2 ), 107–115. 10.1016/j.bpsc.2017.08.003 29529405
Silani, G., Lamm, C., Ruff, C. C., & Singer, T. (2013). Right supramarginal gyrus is crucial to overcome emotional egocentricity bias in social judgments. The Journal of Neuroscience, 33 (39 ), 15466–15476. 10.1523/JNEUROSCI.1488-13.2013 24068815
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18 (6 ), 643–662. 10.1037/h0054651
The MathWorks Inc. 2020. MATLAB version: R2020b. The MathWorks Inc. https://www.mathworks.com
Van der Elst, W., Van Boxtel, M. P. J., Van Breukelen, G. J. P., & Jolles, J. (2006). The Stroop color-word test: Influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment, 13 (1 ), 62–79. 10.1177/1073191105283427 16443719
Veer, I. M., Oei, N. Y. L., van Buchem, M. A., Spinhoven, P., Elzinga, B. M., & Rombouts, S. A. R. B. (2015). Evidence for smaller right amygdala volumes in posttraumatic stress disorder following childhood trauma. Psychiatry Research: Neuroimaging, 233 (3 ), 436–442. 10.1016/j.pscychresns.2015.07.016
Wang, G. (2013). The research of amount and developing trends of “only-child-death”. Chinese Journal of Population Science, 16 (1 ), 57–65.
Xu, X., Wen, J., Skritskaya, N. A., Zou, X., Mauro, C., Wang, J., & Shear, M. K. (2022). Grief-related beliefs in Shidu parents with and without prolonged grief disorder: Psychometric properties of a Chinese version of the typical beliefs questionnaire. Clinical Psychology & Psychotherapy, 29 (2 ), 512–523. 10.1002/cpp.2641 34235799
Yan, C.-G., Wang, X.-D., Zuo, X.-N., & Zang, Y.-F. (2016). DPABI: Data processing & analysis for (resting-state). Brain Imaging. Neuroinformatics, 14 (3 ), 339–351. 10.1007/s12021-016-9299-4 27075850
Yin, Q., Shang, Z., Zhou, N., Wu, L., Liu, G., Yu, X., Zhang, H., Xue, H., & Liu, W. (2018). An investigation of physical and mental health consequences among Chinese parents who lost their only child. BMC Psychiatry, 18 (1 ), 45. 10.1186/s12888-018-1621-2 29433470
Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022). Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review. NeuroImage, 249 , 118870. 10.1016/j.neuroimage.2021.118870 34979249
Zhang, M. Y., Katzman, R., Salmon, D., Jin, H., Cai, G. J., Wang, Z. Y., Qu, G. Y., Grant, I., Yu, E., & Levy, P. (1990). The prevalence of dementia and Alzheimer’s disease in Shanghai, People’s Republic of China: Impact of age, gender, and education. Annals of Neurology, 27 (4 ), 428–437. 10.1002/ana.410270412 2353798
Zhou, N., Wen, J., Stelzer, E.-M., Killikelly, C., Yu, W., Xu, X., Shi, G., Luo, H., Wang, J., & Maercker, A. (2020). Prevalence and associated factors of prolonged grief disorder in Chinese parents bereaved by losing their only child. Psychiatry Research, 284 , 112766. 10.1016/j.psychres.2020.112766 31951871
Zhu, X., Yuan, F., Zhou, G., Nie, J., Wang, D., Hu, P., Ouyang, L., Kong, L., & Liao, W. (2021). Cross-network interaction for diagnosis of major depressive disorder based on resting state functional connectivity. Brain Imaging and Behavior, 15 (3 ), 1279–1289. 10.1007/s11682-020-00326-2 32734435
Zimmermann, J., Ritter, P., Shen, K., Rothmeier, S., Schirner, M., & McIntosh, A. R. (2016). Structural architecture supports functional organization in the human aging brain at a regionwise and network level. Human Brain Mapping, 37 (7 ), 2645–2661. 10.1002/hbm.23200 27041212
