The predictive role of frontoparietal network in clinical symptoms and treatment response in anorexia nervosa

doi:10.21203/rs.3.rs-3218402/v1

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The predictive role of frontoparietal network in clinical symptoms and treatment response in anorexia nervosa

https://doi.org/10.21203/rs.3.rs-3218402/v1

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Objective

Anorexia nervosa (AN) has been characterized as an over-control disorder with increased resting-state functional connectivity (RSFC) in the frontoparietal control network (FPCN). However, there has been limited exploration of neuroimaging biomarkers for predicting treatment response in AN. The study aimed to investigate the role of FPCN in predicting clinical symptoms and treatment response in AN patients.

Method

Seventy-nine female AN patients who had not received prior treatment and forty matched healthy controls (HCs) were recruited for this study. All participants completed the Questionnaire Version of the Eating Disorders Examination (version 6.0) to assess the severity of AN. Additionally, RSFC data were obtained from all participants at baseline. AN patients then underwent outpatient treatment for four and twelve weeks, during which time their clinical symptoms were evaluated using the same measures as at baseline.

Results

After controlling for depression and anxiety symptoms, the AN group still exhibited increased RSFC in the right posterior parietal cortex (PPC) and dlPFC compared to HCs. Moreover, RSFC in the PPC to inferior frontal gyrus (IFG) was found to be an important predictor or tend to be an important predictor for the clinical symptoms after controlling for age, and treatment response of AN after controlling for age, age of onset and BMI.

Conclusion

Alterations in RSFC of FPCN may provide a possible neural marker to assist in predicting clinical symptoms and treatment response in AN patients, providing new insight into the pathogenesis of AN and allowing for early prevention and treatment.

Anorexia nervosa

clinical symptoms

treatment response

resting-state functional connectivity

frontoparietal control network

Anorexia nervosa (AN) is an eating disorder characterized by severe dietary restriction, misperceptions of body shape and weight, fear of weight gain, combined with some emotional problems [1], with the highest and increasing mortality rates (5–6%) among psychiatric illnesses, and especially threatens the physical and psychological health of female adolescents [2]. The symptoms of AN is various, the ability of impulse control and the response to emotional stimuli are closely associated with the risk of binge-eating and purging episodes [3, 4], the distorted body image and excessive focus on food intake and body shape was closely related to the enhanced cognitive control function [5, 6]. The frontoparietal control network (FPCN) was reported to be involved in the process of cognitive and impulsive control in humans [7]. Alterations in resting-state functional connectivity (RSFC) of the frontoparietal control network were studied widely to be implicated in psychiatric disorders and symptoms [8, 9], which was also associated with the aetiology of AN [10]. However, there is currently no specific treatment approach showing clear superiority efficacy for AN. Moreover, AN is prone to relapse and lacks effective predictive markers for treatment efficacy; therefore, it is necessary to further investigate its pathogenesis and predictive markers [11, 12].

AN has been described as a disorder with excessive cognitive control functions associated with its related altered brain network may contribute to the development and maintenance of the disorder [6]. The impulses of AN have been shown to be correlated with its resting state [13]. The frontoparietal control network (FPCN) was reported to be involved in the process of cognitive functions and impulsive control in humans [7]. Specifically, alterations in the FPCN during the resting state have been studied widely and implicated in psychiatric disorders and symptoms [8, 9], including AN [10]. On the other hand, The FPCN regulates eating behaviors, and the altered brain activation in the FPCN has been shown be associated with abnormal eating behaviors [14, 15]. Patients with AN exhibited widespread alteration in executive function and impaired cognitive flexibility which appeared to be linked to aberrant FPCN function [13].

The FPCN, generally also known as the central executive network (CEN) or cognitive control network, encompassing brain regions of the bilateral posterior parietal cortex (PPC) and bilateral dorsal lateral prefrontal cortex (dlPFC), is a flexible hub with a high degree of connectivity across the brain [16, 17]. Alterations in brain-wide connectivity in the FPCN were linked with a wide range of mental illnesses and may play a crucial role in promoting and maintaining mental disease [9, 18], including AN [5]. Previous study suggested that neural activities in the frontal and parietal brain were associated with enhanced abilities of inhibition and cognitive control [14], and increased RSFC within the FPCN might be linked with excessive cognitive control in AN [10]. Therefore, the dysfunction of the FPCN may be an important neuropathological factor in AN.

In addition, previous studies investigated the relationship between the antidepressant treatments, psychiatric symptoms and FC between brain regions. It showed that the altered RSFC related with FPCN could predict antidepressants medications and psychological treatment outcome in patients with major depressive disorder [19–21], post-traumatic stress disorder (PTSD) [20], and attention deficit hyperactivity disorder (ADHD) [22]. Remission of the disease was achieved by normalizing abnormal regional connections through treatment, including the brain regions in FPCN[23]. While the predictive role for treatment response in AN has not been studied.

Given above, the FPCN was robustly related to cognitive control, executive function and abnormal eating symptoms in AN. In previous studies, there is no study to predict neurological markers of clinical outcome in AN patients. Considering the urgent need to explore the neural mechanisms of AN pathogenesis and clinical treatment. Thus, the present study holds the following hypothesis: AN is associated with altered RSFC in the FPCN compared to HC, and the RSFC in the FPCN may be a neural marker to predict the clinical symptoms and treatment response of AN.

Study design

This was a case-control study. All participants underwent physical examination, clinical questionnaire, resting-state functional magnetic resonance imaging (fMRI) examinations and BDNF Val66Met polymorphism detection at baseline. Meanwhile, patients received routine outpatient treatment, which mainly included psychological counseling, nutritional evaluation, and pharmacological treatments based on their situations. The pharmacological treatments mainly include fluoxetine 10-30mg/d and quetiapine 10mg/d, which improves the patient's abnormal eating symptoms, mood symptoms, and distorted body image. Benzodiazepines (including alprazolam, lorazepam and estazolam) and zolpidem were allowed to be used to improve anxiety and sleep. Physical examination and clinical symptoms were evaluated at the fourth and twelfth week for follow-up patients. Ultimately, 40 patients completed the four-week follow-up and 35 patients completed the twelve-week follow-up. A schematic diagram of the research process is shown in Fig. 1.

Participants

Patients with AN were recruited from January 2015 to March 2022 at Shanghai Mental Health Center (SMHC) according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), of whom forty-seven were AN-R subtypes and thirty-two were AN-BP subtypes. Healthy controls (HCs) recruited among students and health workers by advertisements were matched to patients in terms of age and education level. The following inclusion criteria were applied to all patients: females aged 13 to 30 years, Chinese Han ethnicity, right-handed, no cognitive impairments, body mass index (BMI) changing from 13.0 kg/m² to18.5 kg/m², no evidence of serious or chronic somatic disease and other psychiatric disorder history, absence of the use of psychiatric and hormone drugs at least for twelve weeks prior to the study, not currently pregnant or breastfeeding and no metal implants in their bodies that would prevent them from undergoing MRI examination. The criteria were applied to HCs, except that they did not have a current or past eating disorder diagnosis.

Ethical considerations

The study was approved by the Research Ethics Committee of Shanghai Mental Health Center (SMHC), Shanghai Jiaotong University, China (Ethical Approval No.2018-28; 2021ky-169). All participants or their parents signed the informed consent form.

Outcome assessments

Demographic and clinical characteristics, including age, education level and BMI (calculated by current weight and height), were collected from all subjects. The severity of ED was measured as follows:

Version of the Eating Disorders Examination (EDE-Q 6.0)

The EDE-Q6.0 scale that has twenty- eight items is widely used to assess the symptoms and behaviors of eating disorder (ED) over the past twenty- eight days. The scale includes 6 open-ended items and 22 attitudinal items which consists of four subscales: restraint, eating concern, shape concern, and weight concern. Our previous study has confirmed that the reliability and validity of the scale in mainland China were good with the Cronbach α coefficients of 0.95 and test-retest reliability of 0.73 [24].

Magnetic resonance imaging acquisition and preprocessing

All images were acquired on a 3.0T Simens Verio MRI scanner (Enlargen, Germany) with a 12-channel head coil at SMHC. Resting-state scans were acquired using a gradient echo-planar imaging (GRE-EPI) sequence (number of slices = 45, 170 scans, TR = 3000 ms, TE = 30 ms, flip angle = 85, FOV = 216 mm, matrix = 64 × 64, voxel size = 3*3*3 mm, slice thickness = 3.0 mm). High-resolution anatomical scans were acquired with a T1-weighted 3D MP-RAGE sequence (192 sagittal slices, TR = 2300 ms, TE = 2.96 ms, flip angle = 85, FOV = 256 mm, matrix = 256 × 256, slice thickness = 1 mm). Before scanning, participants were instructed to stay still and awake with their eyes closed. Foam paddings and earplugs were provided to control head motion and reduce scanner noise.

Image preprocessing and analysis were conducted using SPM 12 (http://www.fil.ion.ucl.ac.uk/spm) and CONN toolbox 20.b (http://www.nitrc.org/projects/conn) on MATLAB R2021b (Mathworks Inc). The fMRI preprocessing steps were as follows: discarding of the first 10 volumes, motion correction, realignment, slice-timing correction, outlier scans for scrubbing, functional and structural images were segmented by grey/white/CSF and then normalized into Montreal Neurological Institute space. Functional mages were spatially smoothed with a Gaussian kernel of 6 mm FWHM and the bandpass filtering was 0.008–0.09 HZ. Potential confounders such as white matter, CSF, realignment, scrubbing, and effect of rest were regressed out.

Region of interest (ROI) selection and quality check

FPN seeds were identified using the Harvard-Oxford Cortical Structural atlas, and brain regions were defined using Brodmann area (BA) atlas, including four brain regions of the bilateral PPC and dlPFC. The PPC seed was explored one brain region located at BA 48, and the dlPFC seed was explored three brain regions located at BA 8, BA 25 and BA 48. Seed-based correlation analysis was used to investigate time series connectivity between the seeds in FPCN and all other voxels in the whole brain, and the connection strength was extracted from all participants.

The quality of brain image values for each participant was checked based on the registration quality and head motion. Head motion was assessed using mean framewise displacement (mean FD) whose maximal value was set to 0.5mm. Those with poor image quality were excluded from further analysis. Finally, all participants passed the image quality check.

This study led to a sample size of 79 patients and 40 HCs who passed quality checks of clinical questionnaires and brain images.

Statistical Analyses

The demographic and clinical characteristics were analyzed using SPSS 25.0 (Chicago, USA). Continuous data are presented as the mean (SD), and categorical data are presented as numbers or percentages. Group comparisons were conducted using independent-sample t-tests or Mann-Whitney U tests for continuous variables, and chi-square test for categorical variables. Pearson’s correlation was used to assess the correlation between EDE-Q total score, treatment response and RSFC in seeds of the FPCN. Treatment response was expressed as the decrease or decrease rate of EDE-Q6.0 total score in sensitive analysis. Multiple linear regression was conducted with the EDE-Q6.0 total score at baseline, the decrease of EDE-Q6.0 total score at the fourth week and the twelfth week as the dependent variables respectively, RFSC in the PPC-inferior frontal gyrus (IFG) as independent variables, and age, age of onset, BMI as covariates. All multiplied tests were two-tailed and P < 0.05 was considered statistically significant.

A seed-to-voxel analysis was adopted to explore RSFC between AN and HC groups using the CONN toolbox. Fischer-transformed correlation coefficients were generated between bloodoxygen-level-dependent (BOLD) time series in seeds within the FPCN and all other voxels in the whole-brain to create functional connectivity maps. A general linear model (GLM) was used to explore the differences in functional connectivity between AN and HC groups. BDI-Ⅱ total score and BAI total score were included as covariates to exclude their influence on results. Connectivity differences were considered significant between the two groups when a voxel height threshold was P < 0 .001 and a cluster-size threshold was corrected at a P < 0.05 false discovery rate (FDR).

Demographic and clinical characteristics

A total of 79 AN and 40 HC participants completed the study, among which 40 patients completed the follow-up for four weeks, and 35 patients completed the follow-up for twelve weeks; the other patients did not attend the follow-up, possibly due to the extremely inconvenient access to medical care during the COVID-19 outbreak or reluctance to continue after improvement. There were no significant differences in age, education level between the two groups. The BDI scale (P < 0.001), BAI scale (P < 0.001) and EDE-Q6.0 (P < 0.001) were higher for AN patients than for the HCs. As expected, patients with AN had a significantly lower BMI (P < 0.001) than the HCs (Table 1).

Table 1

Demographic and clinical characteristics
		Mean (SD)
Characteristics		AN	HC	Test	P value
No.		79	40
Age(years)		18.92 (3.88)	20.08 (2.41)	t = -1.75	0.083
Age of onset		16.63 (3.55)	-	-	-
BMI		15.19 (2.10)	19.87 (2.64)	t = -10.49	< .0001
BDI-Ⅱ total score		21.22 (16.69)	6.15 (11.68)	Z = 5.88	< .0001
BAI total score		16.75 (15.97)	3.21 (5.19)	Z = 6.78	< .0001
EDE-Q 6.0 total score (beseline)		2.46 (1.60)	0.77 (0.66)	Z = 8.07	< .0001
	Restraint	2.28 (1.92)	0.72 (1.07)	Z = 5.69	< .0001
	Eating concern	2.43 (1.81)	0.27 (0.33)	Z = 10.30	< .0001
	Shape concern	2.73 (1.74)	1.14 (0.85)	Z = 6.71	< .0001
	Weight concern	2.38 (1.74)	0.96 (0.88)	Z = 5.94	< .0001
No.		40
EDE-Q 6.0 total score (first month)		1.50 (1.45)	-	-	-
	Restraint	0.98 (1.02)	-	-	-
	Eating concern	1.35 (1.23)	-	-	-
	Shape concern	1.98 (1.56)	-	-	-
	Weight concern	1.71 (1.45)	-	-	-
No.		35
EDE-Q 6.0 total score (third month)		1.34 (1.23)	-	-	-
	Restraint	0.75 (1.04)	-	-	-
	Eating concern	1.15 (1.25)	-	-	-
	Shape concern	1.82 (1.60)	-	-	-
	Weight concern	1.63 (1.58)	-	-	-
Abbreviations: AN, anorexia nervosa; HC, healthy control; BMI, body mass index; EDE-Q6.0, Questionnaire Version of the Eating Disorders Examination; BDI, beck depression inventory; BAI, beck anxiety inventory. For continuous variables comparisons, the homogeneity variance satisfied is denoted by t, and not satisfied is denoted by t’.

Resting state functional connectivity

According to seed-to-voxel analyses, RSFC maps in FPCN seeds showed significant differences between AN and HC. Significantly increased RSFC (P < 0.05, FDR-corrected) was observed in AN compared with HC between the right PPC seed and the left IFG (x=-42, y = 06, z = 20, t = 5.48, k = 117, size P = 0.029), the right dlPFC seed and middle temporal gyrus (x=-48, y=-20, z=-6, t = 5.16, k = 111, size P = 0.032), the posterior division left (x=-6, y = 38, z = 48, t = 5.56, k = 102, size P = 0.032), and the subcallosal cortex (x=-2, y = 18, z=-6, t = 5.62, k = 96, size P = 0.032) (Fig. 2 and Table 2). No significant differences were found in RSFC maps generated from the seed of the left PPC and the left dlPFC. Significant results for the dlPFC seed are presented in Fig. 2. Furthermore, no significant differences in the RSFC maps of FPN seeds were found between the AN-R and AN-BP subtypes.

Table 2

Characteristics of significant RSFC clusters in FPCN.
	MNI coordinates			Cluster size (voxels)	df	t	Size P-FDR corrected	Main region
Seeds	x	y	z	Cluster size (voxels)	df	t	Size P-FDR corrected	Main region
Right PPC	-42	06	20	117	117	5.48	0.029	Inferior Frontal Gyrus, pars opercularis Left
Left PPC
Right dlPFC	-48	-20	-6	111	117	5.16	0.032	Middle Temporal Gyrus, posterior division Left
	-6	38	48	102	117	5.56	0.032	Superior Frontal Gyrus Left
	-2	18	-6	96	117	5.62	0.032	Subcallosal Cortex
	20	-4	30	91	117	-5.12	0.032
Left dlPFC
Abbreviations: RSFC, resting-state functional connectivity; FPCN, frontoparietal control network; MNI, Montreal Neurological Institute; FDR, False Discovery Rate; PPC, posterior parietal cortex; dlPFC, dorsal lateral prefrontal cortex. Cluster size FDR-corrected P < 0.05 and a voxel height threshold of P < 0.001.

Correlations between RSFC in different brain regions and clinical variables

Pearson’s correlation analysis was conducted to explore associations between z scores of clusters that showed significant differences in RSFC of FPCN seeds and relevant clinical variables at baseline in both AN and HC groups. Significant correlations were found between the PPC to IFG connectivity strength and the EDE-Q6.0 total score at baseline (r=-0.0335, P = 0.003) in AN patients. No significant correlations were found between RSFC in the FPCN and clinical variables in HCs (Fig. 3a).

Treatment response was expressed as the decrease of EDE-Q6.0 total score between baseline and the corresponding treatment node. Correlation analysis was performed between treatment response and five different brain regions, and showed that only RFSC of PPC-IFG had an approximate significant or significant correlation with treatment response at both the fourth week (r=-0.278, P = 0.083) and the twelfth week (r=-0.416, P = 0.013) (Fig. 3b-c).

Furthermore, multiple linear regression was conducted with the EDE-Q6.0 total score at baseline (F = 5.161, P = 0.008, R² = 0.122), RFSC in the PPC-IFG as independent variables, and age as covariates. Multiple linear regression was conducted with thetreatment response at the fourth week (F = 3.737, P = 0.061, R² = 0.378), and the twelfth week (F = 3.098, P = 0.039, R² = 0.383) as the dependent variables respectively, RFSC in the PPC-IFG as independent variables, and age, age of onset, and BMI as covariates. As shown in Table 3, RFSC in PPC-IFG was significant factor for both the clinical symptoms at baseline and the treatment response at the fourth week and the twelfth week in AN.

Table 3

Results of linear regression
	Unstandardized coefficients		Standardized coefficients	t	P	95%CI
	B	Std. Error	Standardized coefficients	t	P	upper	lower
Dependent variable: the decrease of EDE-Q6.0 total score at baseline
Constant	2.952	0.756		3.90	0	1.445	4.459
PPC-IFG	-4.65	1.447	-0.356	-3.21	0.002	-7.534	-1.766
Age	-0.024	0.039	-0.066	-0.60	0.551	-0.102	0.055
Dependent variable: the decrease of EDE-Q6.0 total score at the fourth week
Constant	5.047	3.14		1.61	0.125	-1.551	11.644
PPC-IFG	-4.435	1.97	-0.472	-2.25	0.037	-8.574	-0.297
Age	0.214	0.176	0.617	1.22	0.24	-0.156	0.585
Age of onset	-0.49	0.245	-1.042	-2.01	0.06	-1.004	0.023
BMI	0.008	0.141	0.011	0.06	0.957	-0.288	0.304
Dependent variable: the decrease of EDE-Q6.0 total score at the twelfth week
Constant	6.241	2.81		2.22	0.038	0.381	12.102
PPC-IFG	-6.835	2.782	-0.467	-2.46	0.023	-12.639	-1.032
Age	0.007	0.079	0.024	0.09	0.928	-0.158	0.172
Age of onset	-0.285	0.147	-0.497	-1.95	0.066	-0.591	0.021
BMI	-0.04	0.135	-0.058	-0.296	0.77	-0.323	0.243

To our knowledge, the present study is the first to investigate the predictive role of significant seeds in FPCN on clinical symptoms and treatment response in AN patients. First, after controlling for depression and anxiety symptoms, RSFC from the PPC and dlPFC of FPCN was increased in patients with AN versus HCs. Second, after controlling for age, the RSFC of PPC to IFG may be a predictive neural marker of clinical symptoms, and after controlling for age, age of onset and BMI, the RSFC of PPC to IFG may be a predictive neural marker of treatment response in AN. While no significant differences in RSFC of the FPCN were found between AN-R and AN-BP patients, so it may not be a neural marker to differentiate the two subtypes of AN. Thus, these results provide evidence for the importance role of the PPC brain in the FPCN for AN.

RSFC in the PPC to IFG was increased in AN versus HC in the study, adding support for the description of AN as a disorder of excessive cognitive control, which was in line with a previous study [10]. FPCN engages in a wide variety of executive functions by allocating the top-down attentional resources to arrange cognitive control processes [25]. The impaired cognitive control ability was consistent with previous neuropsychological finding of decreased cognitive flexibility and hyper-detailed information processing in AN patients, which may be related to impaired executive control networks in the brain[13, 26]. Besides, alterations in FPCN connectivity help humans perform various tasks adaptively by rapidly modifying functional connections according to current goals, which also shows its flexible role in cognitive control [27]. A study confirmed that FPCN functional connectivity contributed to the loss of control in patients with binge drinking, possibly by impairing cognitive function and response inhibition [28]. In addition, the right IFG is involved in inhibiting control and stopping the upcoming impulsive responses, and the impairment of the right IFG is closely related to impaired inhibitory control[29]. The above studies contribute to our understanding of the role of FPCN in the pathogenesis of AN.

Moreover, the study showed changed RSFC in FPCN was related to the abnormal eating symptoms in ANs while not in HCs, supporting the hypothesis that the RSFC in FPCN may contribute to the regulation of pathological symptoms in ANs. The alteration of FPCN neural function may be an important factor in causing the pathological symptoms and psychological characteristics of patients, which was consistent in our finding [30]. The FPCN system is responsible for a range of cognitive functions and regulates eating behaviors. Lower FC in the FPCN was associated with worse eating self-control, further contributing to disturbed eating disorders [31]. Besides, PPC and dlPFC of FPCN are involved in regulating eating behaviors through cognitive control in the face of delicious food [32]. The food consumption of AN patients was suggested to be closely related to the dlPFC and dorsal striatal connectivity [30]. The above studies provided the theoretical basis for our results.

The current study also showed the aberrant RSFC in the PPC to IFG was negatively associated with the treatment response of AN, and remained a significant predictor of treatment response after controlling for the age, age of onset and BMI. AN patients exhibited excessive concerns about body shape and weight, as well as excessive control over diet, and a stronger RSFC in FPCN than in HCs may explain why a stronger RSFC in FPCN implies poorer clinical outcomes. Recent studies have shown that alterations in FPCN function were strongly linked with the clinical consequences of depression, possibly by affecting cognitive control [19, 33]. It has been argued that treatment response was associated with altered connectivity within and between networks, including the FPCN [21]. The brain dysfunction of the networks could lead to pathological symptoms and the normalization of aberrant regional connectivity allows patients with MDD to achieve remission of the disease after treatment [16]. Abnormal activity of the dlPFC in FPCN was one of the starting points for efficacy prediction studies in MDD [34]. Interestingly, the dlPFC has also been reported to be an important target for AN [35], and the PPC may also be a significant target for AN patients in the current study, suggesting that it is as an alternative target for intervention that could be considered in future treatment protocols. Unfortunately, the study did not track the FPCN function after treatment, which needs to be further improved in the future.

The study failed to find significant differences in the RSFC of FPCN between the AN-R and AN-BP subtypes. Long-term follow-up study on patients with different subtypes of AN showed that most patients with AN-R have an early age of onset, most AN started with AN-R, about 88% of AN-R can develop bulimic behaviors, 62% of AN-R eventually develops into AN-BP subtype [36]. Based on our results, we conjecture that the RSFC of the FPCN is altered in the early onset of AN. Moreover, most patients tend to be in the adolescent stage, and not yet adults, when the neural activity of the brain is still at a mature stage of development and the brain is more plastic in structure and function than in adults [37], which may explain the short-term improvement of clinical symptoms.

There are a number of limitations in the present study. First, the study was based on the cross-sectional analysis that could not determine the causation, and it was not clear how the RSFC of FPCN changed after treatment. Second, the study did not take the effect of undernutrition on brain networks into account. Third, the rate of shedding at subsequent follow-up was relatively high mainly due to the strict management of population movements and transport restrictions during the COVID-19 pandemic in China, and the BMI was not tracked. Fourth, the treatment response of AN was expressed as a reduction rather than a reduction rate of EDE-Q6.0 total score, which may increase the power to predict outcome. Considering that this study is the first to explore the neurobiological mechanisms for treatment response in AN, future research with larger samples and longitudinal design is needed to validate our findings.

The current study provides the evidence that the RSFC in FPCN is increased in patients with AN versus HCs after controlling for emotional symptoms, and yielded initial indication that the RSFC in PPC to IFG may be a predictive neural marker for clinical symptoms and treatment response of AN. These findings are helpful in further understanding the pathogenesis of AN, as well as open up potential avenues for relevant target strategies for prevention and treatment.

Acknowledgements We would like to express our sincere gratitude to all participants who made contributions to the study and all of the editors and reviewers.

Author Contributions Qianqian He: Methodology, Formal analysis, Writing- Original draft preparation, and Visualization. Hui Zheng: Conceptualization, Writing- Reviewing and Editing. Jialin Zhang and Ling Yue: Writing- Reviewing and Editing. Ling Yue, Qing Kang, Cheng Lian, Lei Guo, Yan Chen, Yanran Hu, Yuping Wang and Sufang Peng: Data curation and Resources. Zhen Wang: Project administration, Jue Chen: Funding and Supervision. All authors read and approved the final manuscript.

Funding The study was supported by Grants from Shanghai Jiao Tong University (YG2022ZD026); National Natural Science Foundation of China (81771461, 82071545); Shanghai Hospital Development Center (SHDC12023122); Science and Technology Commission of Shanghai Municipality (20Y11906500); Shanghai Clinical Medical Research Center for Psychiatric and Psychological Disorders (19MC1911100).

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

Conflict of interest The authors declare that they have no competing interests.

Ethical approval The study was approved by the Research Ethics Committee of Shanghai Mental Health Center, Shanghai Jiaotong University, China (Ethical Approval No.2018-28; 2021ky-169). All participants or their parents signed the informed consent form.

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No competing interests reported.

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The predictive role of frontoparietal network in clinical symptoms and treatment response in anorexia nervosa

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Abstract

Objective

Method

Results

Conclusion

Figures

INTRODUCTION

METHODS

Study design

Participants

Ethical considerations

Outcome assessments

Version of the Eating Disorders Examination (EDE-Q 6.0)

Other questionnaires

Magnetic resonance imaging acquisition and preprocessing

Region of interest (ROI) selection and quality check

Statistical Analyses

RESULTS

Demographic and clinical characteristics

Resting state functional connectivity

Correlations between RSFC in different brain regions and clinical variables

DISCUSSION

CONCLUSION

DECLARATIONS

REFERENCES

Additional Declarations

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