The psychopathology of patients with anorexia nervosa has been hypothesized to involve inappropriate self-referential processing, disturbed interoceptive awareness, and excessive cognitive control, including distorted self-concern, disregard of their own starvation state, and extreme weight-control behavior. We hypothesized that the resting-state brain networks, including the default mode, salience and frontal-parietal networks, might be altered in such patients, and that treatment might normalize neural functional connectivity, with improvement of inappropriate self-cognition. We measured resting-state functional magnetic resonance images from 18 patients with anorexia nervosa and 18 healthy subjects before and after integrated hospital treatment (nourishment and psychological therapy). The default mode, salience, and frontal-parietal networks were examined using independent component analysis. Body mass index and psychometric measurements significantly improved after treatment. Before treatment, default mode network functional connectivity in the retrosplenial cortex and salience network functional connectivity in the ventral anterior insula and rostral anterior cingulate cortex were decreased in anorexia nervosa patients compared with those in controls. Interpersonal distrust was negatively correlated with salience network functional connectivity in the rostral anterior cingulate cortex. Default mode network functional connectivity in the posterior insula and frontal-parietal network functional connectivity in the angular gyrus were increased in anorexia nervosa patients compared with those in controls. Comparison between pre- and post-treatment images from patients with anorexia nervosa exhibited significant increases in default mode network functional connectivity in the hippocampus and retrosplenial cortex, and salience network functional connectivity in the dorsal anterior insula following treatment. Frontal-parietal network functional connectivity in the angular cortex showed no significant changes. The findings revealed that treatment altered the functional connectivity in several parts of default mode and salience networks in patients with anorexia nervosa. These alterations of neural function might be associated with improvement of self-referential processing and coping with sensations of discomfort following treatment for anorexia nervosa.
Citation: Gondo M, Kawai K, Moriguchi Y, Hiwatashi A, Takakura S, Yoshihara K, et al. (2023) Effects of integrated hospital treatment on the default mode, salience, and frontal-parietal networks in anorexia nervosa: A longitudinal resting-state functional magnetic resonance imaging study. PLoS ONE 18(5): e0283318. https://doi.org/10.1371/journal.pone.0283318
Editor: Kenji Hashimoto, Chiba Daigaku, JAPAN
Received: September 20, 2022; Accepted: March 6, 2023; Published: May 30, 2023
Copyright: © 2023 Gondo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Due to the limitations of the consent provided by the subjects in our study, we cannot disclose the data to the public. Only researchers who have formally applied to and been approved by the human research ethics committee of Kyushu University Hospital can access the data (firstname.lastname@example.org).
Funding: This work was supported by MEXT KAKENHI Grant Number JP26460910, JP15K08921, and AMED Grant Number JP23dm0307104. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Anorexia nervosa (AN) is characterized by extremely low body weight, intense fear of weight gain, body image distortion, and extreme weight-control behavior. Individuals suffering from this condition exhibit impaired cognition when self-evaluating their body weight and shape, an inability to recognize the dangers posed by their current low body weight and malnutrition , high levels of alexithymia , and disturbances in social emotional functioning [3, 4]. Inappropriate self-referential processing, disturbed interoceptive awareness, and excessive cognitive control have been hypothesized to constitutes the basis of the psychopathology of patients with AN . Excessive concentration on body shape and weight can restrict the spheres of life that patients with AN are able to engage in, such as important normative age-graded experiences and introspection, resulting in interpersonal deficits .
Understanding the brain function exhibited by patients with AN may be helpful for clarifying the underlying neurobiological mechanisms involved in the disorder. There is a need to increase understanding of the comprehensive neural networks of these psychopathologies in patients with AN. Therefore, we focused on intrinsic network functional connectivity (FC) in resting-state functional magnetic resonance imaging (rsfMRI) analysis, which maps functionally connected brain networks, on the basis of spontaneous non-task related fluctuations of blood oxygen level-dependent (BOLD) signals in the resting brain [7, 8]. Resting-state networks (RSNs) are sets of brain areas exhibiting strong FC (the cross-correlation between BOLD signals in different regions in the resting brain), which play specific functional roles in brain activity at rest [9, 10].
It has been suggested that self-referential emotional processing occurs in the default mode network (DMN) , which comprises the precuneus, posterior cingulate cortex (PCC), retrosplenial cortex (RSC), medial prefrontal cortex, lateral temporal cortex, inferior parietal lobule, and hippocampal formation , parts of which have been generally related to emotion processing . The DMN exhibits vigorous activity during rest . The DMN is associated with self-processing and self-consciousness, and may thus be relevant to introspection [11, 14, 15].
The DMN is deactivated antagonistically when the frontal-parietal network (FPN) is active, including the dorsolateral prefrontal cortex and parietal cortices , which underlie executive functioning, such as working memory and goal-oriented (top-down) cognition [17–19].
Another important network, the salience network (SN), plays a role in switching between the DMN and FPN. The SN consists of the anterior insula (AI) and anterior cingulate cortex (ACC); the former detects salient events of interoceptive and exteroceptive sensation and emotion, whereas the latter facilitates coping with these events . We focused on the DMN and associated networks (i.e., the SN and FPN) to reveal the neural pathology of self-referential function, interoceptive awareness, and cognitive regulation in patients with AN.
In previous studies of AN, analyses of the DMN have reported different results in subjects with AN in different states at different disease stages [21–26]. Individuals with AN in the disease state have been reported to exhibit less DMN FC in the precuneus, whereas individuals who had recovered from AN exhibited no difference in DMN FC compared with healthy controls (HCs) .
Previous studies of SN in patients with AN have also reported different results, possibly depending on the disease stage. Network FC involving the ACC was reported to be reduced in patients with current AN [24, 25] and participants who had recovered from AN  compared with that in HCs, whereas some other studies indicated no alteration in the SN FC in participants who had recovered from AN .
In contrast, previous studies of the FPN, representing executive cognitive control, have reported consistent results across disease stages in AN, such as higher FC between the FPN and angular gyrus in both AN patients and in patients who had recovered from AN compared with that in HCs [23, 27]. However, all of the studies mentioned above were cross-sectional. In addition, because current and recovered patients were included in different groups in previous studies, it remains unclear whether individual patients exhibit changes in networks according to the progression of the disease state at the within-subject level.
According to the hypothesis that resting-state FC in patients with AN changes with treatment, two previous studies investigated FC in these patients before and after inpatient weight restoration treatment for 2–4 weeks [28, 29]. In adolescents and young adults with AN, using seed-based analysis, FC of the nucleus accumbens with the orbitofrontal cortex was found to be higher in patients with AN before treatment than in HCs, and was decreased after treatment compared with before treatment [28, 29]. FC between networks (SN-FPN/SN-DMN/DMN-FPN) was not affected by the treatment, whereas the connectivity between SN and FPN was reduced in patients with AN relative to HCs . The treatment strategy adopted in these two previous studies, however, was a short-term, inpatient physical treatment focusing on weight gain, raising the question of whether the treatment had a sufficient therapeutic effect on cognitive changes or psychopathology in patients with AN. Furthermore, the analyses relied only on between-network connectivity, which is not always interpretable. Thus, it remains unclear whether each individual network (DMN, SN, and FPN) exhibited functional changes.
The purpose of the current study was to clarify the longitudinal treatment effect of structural cognitive behavioral therapy standardized for AN, called the “cognitive behavioral approach with behavioral limitation,” which is a type of reinforcement therapy  that is typically conducted for 3–5 months in inpatient treatment. The treatment effect was measured not only using behavioral psychometric scales but also with within-network FC in each of the three different RSNs identified by independent component analyses (ICA) on rsfMRI data. We hypothesized that (1) within-network connectivity in pre-treatment AN patients would be altered in the DMN, SN, and FPN compared with HCs and post-treatment AN patients; (2) these alterations would be associated with individual psychological outcomes related to respective function (self-referential processing, interoception, and cognitive regulation); and (3) pre-treatment AN-specific alterations of RSN would be improved after treatment. Using these combined methods may be helpful for identifying effective treatment targets for AN in the future.
Materials and methods
The current study was approved by the human research ethics committee at Kyushu University Hospital. Written informed consent for the patients’ treatment and these studies was obtained from all participants and from their parents or guardians of minors. All procedures involved in this work complied 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.
All participants with AN and HCs were girls or women, right-handed, and aged between 15 and 50 years old. Thirty AN patients consented to participate in this study and received standardized hospital treatment for AN between 2011 and 2015 at our hospital. Patients were diagnosed with AN according to the DSM-IV by clinicians specialized in eating disorders using the Mini-International Neuropsychiatric Interview, and patients with severe depression, suicidal risk, personality disorders, schizophrenia, or alcohol dependence were excluded. Patients with mild or moderate depression, anxiety, or obsessive-compulsive symptoms were included because these symptoms are often comorbid with AN. Patients with AN were allowed to take their required medications. Data were acquired from patients with AN on the first day and the last day of treatment (pre-AN and post-AN, respectively). Eight patients dropped out of the treatment. The data of four patients were excluded from analysis because of poor registration of the fMRI data. Thus, the final longitudinal sample was 18 patients with AN (eight restricting type, 10 binge eating/purging type).
Eighteen HC subjects were recruited from the local community and were required to have no history of eating disorder or other mental illness. HCs were also required not to take any medications. No longitudinal data were collected from HCs.
Integrated hospital treatment
We treated patients with AN with inpatient therapy called the “cognitive behavioral approach with behavioral limitation” . In this therapeutic approach, patients consented to setting a target body weight and undergoing behavioral limitation (e.g., S1 Table). In addition, patients take part in psychological interviews for their behavioral problems, while they are undergoing nourishment therapy with behavioral limitation. Patients initially received small meals, for easy ingestion. However, if a patient was unable to intake the minimum amount required for nourishment (35 kcal/kg body weight), nasogastric feeding was administered, with patient consent, to compensate for the lack of oral feeding. After confirmation by therapists that a patient was able to ingest the whole meal without difficulty, the amount of the meal was increased gradually by approximately 200 kcal/day, and nasogastric feeding was gradually reduced. Behavioral limitation plays a role in promoting introspection by controlling external stimulation. Gradual removal of behavioral limitations leads to gradual adaptation to real life. S1 Table shows an example of the behavioral limitations and the schedule for lifting them. From the start of the behavioral limitation until the target body weight is reached, the behavioral limitations were lifted step by step as a reward for every kg of weight gain. When a patient reached the target body weight, the next stage of therapy began, providing a rehearsal of real life. In parallel with the therapy, patients received counseling twice a week to learn how to deal with maladjusted behavior, cognition, and emotion. Group therapy and family counseling were also conducted. Through this combined therapy, patients were expected to realize and correct erroneous notions regarding slenderness, eating behavior and interpersonal relationships. They acquired the ability to notice and express their own emotions, and to think about their lives and interpersonal relationships. As necessary, patients were prescribed anti-depressants, anti-anxiety and/or mood stabilizers for mood disturbance and anti-depressants and/or anti-psychotics for severe obsessive behavior.
Demographic and psychometric measurements
Each participant’s body mass index (BMI) was measured, and a subset of participants completed psychometric questionnaires including the self-rating depression scale (SDS)  (pre-AN, n = 15; post-AN, n = 14; HC, n = 18), eating disorder inventory (EDI) (subscales: drive for thinness, bulimia, body dissatisfaction, ineffectiveness, perfectionism, interpersonal distrust, interoceptive awareness, and maturity fears)  (pre-AN, n = 17; post-AN, n = 17; HC, n = 18), and the 20-item Toronto alexithymia scale (TAS-20) (subscales: difficulty in identifying feelings, difficulty in describing feelings, and externally oriented thinking) [33, 34] (pre-AN, n = 17; post-AN, n = 17; HC, n = 18). Group comparisons of these participant characteristics and psychometric measurements were conducted on available data using two-sample t-tests (“pre-AN vs. HC” and “post-AN vs. HC”). Paired-sample t-tests were used to analyze a longitudinal subset of patients who completed questionnaires at pre- and post- AN (“pre-AN vs. post-AN,” SDS, n = 12; EDI, n = 16; TAS-20, n = 16) with IBM SPSS Statistics Version 23.0 (IBM SPSS Inc., Chicago, IL, USA).
Brain image data acquisition
Imaging was performed with a PHILIPS Achieva 3-Tesla scanner (Best, Netherlands). Subjects lay in a supine position, with foam pads fixing the head and earplugs inserted into the ears to reduce head motion and scanner noise. Resting state was defined as when the subject was not engaging in any specific cognitive task during fMRI scanning . During the acquisition of rsfMRI, the subjects were instructed to remain still, relax, close their eyes, and not think anything in particular. Although fMRI was performed at the first procedure with instructions not to fall asleep before the scan, a sleep scale was not used. We obtained the resting-state functional scans using an echo-planar imaging sequence with the following parameters: 32 axial slices, repetition time = 1,793 ms, echo time = 40 ms, fractional anisotropy = 90°, slice thickness/gap = 3/1 mm, field of view = 210 × 210 mm, resolution = 3 × 3 × 4 mm, and 160 volumes in total (4 minutes 54 seconds). High-resolution three-dimensional magnetization-prepared rapid gradient-echo T1-weighted images were acquired for anatomical localization with the following imaging parameters: 200 sagittal slices, repetition time = 7.0 ms, echo time = 3.2 ms, fractional anisotropy = 9°, slice thickness/gap = 1/0 mm, field of view = 256 × 240 mm, resolution = 1 × 1 × 1 mm (6 minutes 31.7 seconds). No participants exhibited structural abnormalities during visual inspection.
Image data preprocessing
Preprocessing of resting-state functional brain images was performed using SPM12 (Statistical Parametric Mapping, Wellcome Trust, UCL, UK). The first 10 volumes of functional images were removed to eliminate the non-equilibrium effects of magnetization. The raw images were converted to the neuroimaging informatics technology initiative (NIFTI) format. Realignment, slice timing correction, and spatial co-registration were performed. According to the exclusion criteria for head motion correction in previous resting state fMRI analyses [36, 37], translational motion parameters were verified to be less than 1 functional voxel. Rotation motion parameters were verified to be less than 2 degrees. Co-registered images were spatially normalized to Montreal Neurological Institute space  with a voxel size of 3 × 3 × 3 mm3 using a standard template in SPM12. The normalized images were then smoothed with an 8 mm full-width at half-maximum Gaussian kernel.
Independent component analysis
RSN consists of brain regions in which neural activities are temporally correlated and considered to be functionally interconnected. To identify RSNs, an ICA was performed to decompose the rsfMRI voxel-by-voxel signals into temporally independent hemodynamic patterns distributed in different regions  using the Group ICA fMRI Toolbox, which operates in Matlab. The number of components was estimated using minimum description length criteria . The dimensionality of the preprocessed rsfMRI data from each subject was reduced using principal component analysis. An ICA using the infomax algorithm was then applied to the data . For each subject, this ICA generated a volumetric map for each component (component image), which contains the contribution of each component’s time course to the BOLD signal in each voxel. The individual component images were reconstructed (back-reconstruction using the GICA algorithm) and converted to z-values , which were fed into subsequent second-level between-subject analyses, as described below.
The spatial distribution of RSNs was identified using a template based on previous studies  obtained from 90 functional regions of interest (fROIs, https://findlab.stanford.edu/functional_ROIs.html). The DMN template comprised the medial prefrontal cortex, PCC, RSC, and medial temporal lobe. The SN template was composed of the AI and dorsal ACC. The FPN template included the bilateral parietal cortex and dorsolateral prefrontal cortex (DLPFC). We chose the component with the highest correlation with the respective template mask.
Mapping network-related connectivity by groups and treatment stages
To map the three different RSNs’ connectivity by groups and treatment stages, whole-brain voxel-by-voxel one-sample t-tests were performed on individual z-transformed component images for each selected component in HCs, pre-AN patients, and post-AN patients (significant at a family-wise error [FWE]-corrected peak-level threshold of p < 0.05) (SPM-12). These RSN maps were used to create the RSN masks in the following analysis.
Group and treatment effect on FC in resting-state networks
To specify the brain regions with AN-specific alterations in FC within each of the three different RSNs of interest, individual component images were compared between pre-AN patients and HCs with second-level group analyses using voxel-by-voxel two-sample t-tests (SPM-12). We employed a cluster defined by an FWE-corrected peak-level threshold of p < 0.05. To ensure that the differences selectively reflected the FC within the network of interest, these analyses were restricted within the respective RSN masks that were created by overlap between the gray matter mask and the conjunction of network-related connectivities in pre-AN patients and HCs (significant at a FWE-corrected peak-level threshold of p < 0.05).
In an exploratory analysis to identify the brain regions exhibiting a treatment effect on FC within each of three different RSNs, individual component images were compared between the pre-AN and post-AN groups in second-level group analyses using voxel-by-voxel paired t-tests (SPM-12). We employed a cluster defined by an FWE-corrected peak-level threshold of p < 0.05. To ensure that the differences reflected the FC within the network of interest selectively, these analyses were restricted within the respective RSN masks that were created from overlap between the gray matter mask and the conjunction of network-related connectivities in the pre-AN and post-AN groups (significant at an FWE-corrected peak-level threshold of p < 0.05). We used the gray matter mask from WFU PickAtlas (http://fmri.wfubmc.edu/software/PickAtlas).
Definition of regions of interest
To further investigate the detailed features of the RSN connectivity in brain regions involved in AN pathology, and to determine whether such pathological connectivity would be changed with treatment, we defined the regions of interest (ROIs) that were specific to AN pathology. The spheres (5 mm radius) centering the peak of significant clusters were detected by comparison between pre-AN patients and HC, and the detected spheres were defined as the ROIs specific to AN pathology. In some cases, regions detected by the comparison were assumed to be small and consisted almost exclusively of peaks. To show that the peaks were not false positives and to correct for this possibility, the regions surrounding the peaks, including the peaks, were defined as the ROI. Next, we calculated the mean of component contribution values within the voxels in ROIs in the individual component image for pre-AN patients, HC, and post-AN patients using Marsbar software . These values represent the strengths of network connectivity to whole brain. The individual mean component values at these ROIs were used for the following two analyses: a multiple regression analysis to identify demographic and psychometric indices related to RSN FC in these ROIs in each of pre-AN patients and HC, and an analysis of treatment effects on RSN FC in these ROIs by comparing the post-AN and pre-AN groups.
Relationship of RSN FC in ROIs to psychometric measurements
Multiple regression analysis was used to evaluate the demographic and psychometric factors that most strongly influenced differences in RSN FC between AN patients and HCs in AN-specific ROIs. Independent variables were set from indices that were significantly different between pre-AN individuals and HCs in psychometric measurements (SDS, EDI subscales, and TAS-20 subscales). Covariates were set from demographic indices (age, education, BMI, disease duration, and medication use). For a subset of participants who completed all psychometric measurements (i.e., pre-AN, n = 15, and HC, n = 18), we set mean contribution values within the ROIs as the dependent variables, psychometric measurements as independent variables selected with the stepwise method, and demographic indices as covariates with the forced entry method using SPSS.
Treatment effects on RSN FC within AN-specific ROIs
In addition to the exploratory analysis of the treatment effect on RSN FC, we investigated the treatment effect on network connectivity within the AN-specific ROIs in each of the different networks of interest. The mean values in the ROIs in individual component images were compared between pre-AN and post-AN patients using paired-sample t-tests (SPM-12, Marsbar), thresholded at a significance level of q < 0.05 with false discovery rate correction for multiple comparisons . Additionally, correlation between a change of RSN FC in the ROIs and increase in BMI by treatment was calculated.
Demographic indices and psychometric measurements
There were no significant differences in age between AN patients and HCs. However, HCs had a longer duration of education than patients. The pre-AN group had significantly lower BMI values than both the post-AN and HC groups. The post-AN group had significantly higher BMI values than the pre-AN group, but had significantly lower BMI values than the HC group (Table 1). Although the post-AN group tended to recover body weight loss, they did not recover enough to reach the normal weight level.
In almost all psychometric measurements, the pre-AN group had more pathological characteristics than the HC group (Table 1). The pre-AN group exhibited higher levels of depressive symptoms (SDS), higher ED pathology (EDI total and all eight subscales) and higher alexithymia (TAS-20 total and its two subscales). There was no difference in TAS-20 externally oriented thinking.
The treatment effect was shown not only by a gain in body weight, but also by improvement of most psychometric measurements. Post-AN scores were significantly decreased for the SDS, EDI total (subscales: drive for thinness, bulimia, body dissatisfaction, ineffectiveness, interoceptive awareness, and maturity fears), and TAS-20 total (subscale: difficulty in identifying feelings), whereas the treatment showed a trend-level effect on perfectionism, interpersonal distrust, and difficulty describing feelings (DDF).
The results revealed that, after treatment, AN patients approached the level of HCs in some psychological measurements: the post-AN group did not significantly differ from the HC group in SDS scores, EDI scores (subscales: drive for thinness, bulimia, body dissatisfaction, perfectionism, interoceptive awareness, and maturity fears), and TAS-20 total scores (difficulty in identifying feelings). However, significant differences in EDI ineffectiveness and EDI total remained between the post-AN and HC groups, despite a significant treatment effect. Significant differences in EDI interpersonal distrust and TAS-20 DDF also remained between the post-AN and HC groups, and a significant treatment effect was not found. This indicates that post-treatment AN patients still exhibited AN-related pathological tendencies for some psychological features.
Component identification and RSN FC maps by groups and treatment stages
ICA extracted 17 independent components, among which three components were identified as RSNs of interest (correlation with the network template, component 12 and DMN: r = 0.473, component 16 and SN: r = 0.388, component 3 and FPN: r = 0.510). RSN FC maps of interest are shown for each group (Fig 1 and S1 Fig). The DMN included clusters in the medial prefrontal cortex, PCC/precuneus/RSC, posterior insula (PI)/transverse temporal gyrus and hippocampus. The PI is not generally considered a major region of the DMN. However, in our study, especially in AN, clusters in the PI were detected as part of the DMN (Fig 1 and S1 Fig). Clusters in the ACC and AI were observed in the SN (S1 Fig). Clusters in the right DLPFC and left angular gyrus (AG) were observed in the FPN (S1 Fig).
Spatial maps are plotted as t statistics thresholded at p < 0.05 and are family-wise error-corrected. MPFC: medial prefrontal cortex, PI: posterior insula, RSC: retrosplenial cortex, PCC: posterior cingulate cortex, Prec: precuneus. See S1 Fig for post-AN DMN and other RSNs.
Exploratory analyses of group differences and treatment effects on FC in RSNs
Comparison between the pre-AN and HC groups revealed AN-specific alterations in the main regions of DMN, SN, and FPN. The pre-AN group showed significantly decreased DMN FC compared with HCs in the RSC (Table 2 and Fig 2A). In the SN, the pre-AN group exhibited significantly decreased FC compared with HCs in the ventral AI (vAI) and rostral ACC (rACC) (Table 2 and Fig 2B, 2C). The pre-AN group showed significantly higher DMN FC than HCs in the PI (Table 2 and Fig 2D), although PI is generally not the main region of DMN. In the FPN, pre-AN exhibited significantly increased FC compared with HC in the AG (Table 2 and Fig 2E).
The pre-AN group exhibited less connectivity than HCs between (A) the DMN and the retrosplenial cortex (RSC) (peak coordinate: 3, −55, 8), (B) the SN and the ventral anterior insula (vAI) (−30, 14, −13), and (C) the SN and the rostral anterior cingulate cortex (rACC) (0, 41, −7). The pre-AN group showed higher connectivity than HCs between (D) the DMN and the posterior insula (PI) (45, −19, 2), and (E) the FPN and the angular gyrus (AG) (−39, −73, 35), Two-sample t-test. Peak-level threshold p < 0.05 family-wise error-corrected.
We found a treatment effect in a few regions within the DMN and SN by comparison of FC maps of interest between the pre-AN and post-AN patients. The post-AN group showed significantly increased DMN FC in the hippocampus (Table 2 and Fig 3A) and SN FC in the dorsal AI (dAI) (Table 2 and Fig 3B) compared with the pre-AN group. No treatment effect was observed in the FPN. We did not identify any regions with FC that was significantly decreased by the treatment.
The post-AN group showed higher connectivity compared with the pre-AN group between (A) the DMN and the hippocampus (27, −19, −16), and (B) the SN and the dorsal anterior insula (dAI) (−27, 17, 11), Paired t-test. Peak-level threshold p < 0.05 family-wise error-corrected.
Relationship of RSN FC in AN-specific ROIs with psychometric measurements
SN FC in the rACC ROI was positively correlated with education (β = 0.441, p = 0.036), and negatively correlated with EDI interpersonal distrust (β = −0.489, p = 0.008). FPN FC in the AG ROI was positively correlated with TAS-20 DDF (β = 0.354, p = 0.034). In the other ROIs, resting-state FC was not significantly related to demographic indices or psychometric measurements.
Pre- and post-treatment comparison of RSN FC in AN-specific ROIs
In addition to the exploratory analysis of treatment effects on RSN FC, we examined the treatment effects on resting-state FC in the AN-specific ROIs in each of the DMN, SN, and FPN. The group comparison of post-AN versus pre-AN exhibited significantly increased FC in the RSC within the DMN (Fig 4A). Treatment effects on the FC were observed as increased connectivity also in the vAI and rACC within the SN, and in the PI within the DMN, although these effects did not remain statistically significant after multiple comparison correction (Fig 4B–4D). Thus, the treatment did not lead to significant improvement of FPN FC in the AG (Fig 4E). A change of RSN FC in any ROI was not significantly correlated with an increase of BMI.
(A) Default mode network, retrosplenial cortex. (B) Salience network, ventral anterior insula. (C) Salience network, rostral anterior cingulate cortex. (D) Default mode network, posterior insula. (E) Frontal-parietal network, angular gyrus. ROI analysis, Paired t-test using Marsbar. * q < 0.05, false discovery rate correction for multiple comparisons.
The current study revealed several main findings, as follows: 1) AN-specific alterations in brain regions within the DMN, SN, and FPN (e.g., the RSC within the DMN, vAI, and the rACC within the SN) that exhibited lower FC in the pre-AN group compared with the HC group. The PI within the DMN and AG within the FPN exhibited high FC in pre-AN in comparison with HC. 2) The SN FC in the rACC ROI was negatively correlated with EDI interpersonal distrust. FPN FC in the AG ROI was positively correlated with TAS-20 DDF. 3) Exploratory analyses for the treatment effect (post-AN versus pre-AN) exhibited increased FC in the hippocampus within DMN and dAI within the SN. When we focused on AN-specific ROIs, the treatment effect was shown as increased FC in the RSC within DMN. Our main hypotheses were supported, as follows: (1) altered within-network connectivity was observed in pre-treatment AN patients. (2) There were relationships between these alterations and individual psychological outcomes. (3) We observed a treatment effect on RSNs.
First, our findings indicated that DMN FC in the RSC showed AN-specific changes and treatment effects. The RSC is known to be structurally and functionally connected to the hippocampus and plays a role in self-reference processing while accessing memory information, such as retrieval of episodic memory, autobiographical memory, navigation with imagination, thinking about the future, introspection, and theory of mind [45–50]. Functional alterations in the RSC have been reported in psychiatric disorders involving impaired self-referential function, such as schizophrenia , bipolar disorder , post-traumatic stress disorder [53, 54], social anhedonia [55, 56], individuals with high-trait-anxiety , and autism . The current results are consistent with a previous study of AN reporting lower DMN FC than HCs in the region containing the RSC with no significant differences between HC and recovered patients . As our behavioral results also revealed a significant recovery in self-cognition function after treatment, low DMN FC in the RSC suggests that impaired self-cognition is a specific feature in AN patients with symptoms. Thus, clinical improvement might be associated with amelioration of DMN FC in the RSC.
The significant recovery of DMN FC in the RSC with our integrated treatment might depend on either nourishment therapy or psychotherapy, or both. This finding could not be explained by a simple correlation with weight gain. Previous studies reported that DMN FC was strengthened by psychotherapy. For example, cognitive behavioral therapy for patients with chronic pain increased the amplitude of low-frequency fluctuation in the cerebellum and PCC (close to the RSC, which is part of the DMN) and FC between these areas . A focused attention meditation increased connectivity from the striatum to the PCC and RSC . Not only recovery of body weight owing to nourishment but also the psychotherapy in our program appeared to exert a therapeutic effect on DMN FC.
With an exploratory analysis, we found that DMN FC in the hippocampus was increased following treatment, even though it was not detected as AN-specific alteration in DMN FC. Hippocampal functional improvement might be associated with progress of introspection in patients with AN. Generally, the hippocampus is coupled with the DMN during memory retrieval , and plays an important role in emotional regulation . The hippocampus is known to be structurally and functionally connected to the RSC [46, 47], so that the treatment-induced increase of DMN FC in the hippocampus observed in our study is considered to be related to improvement in the RSC. Several studies have reported that psychotherapy, including cognitive behavioral therapy, increased hippocampal function in patients with other psychological disorders , such as major depression  and post-traumatic stress disorder . The increased DMN FC in the hippocampus observed in the current study might be also associated with improvement of neural function by repeated introspection in psychotherapy, which is coupled with the improvement of DMN FC in the RSC.
The exploratory analysis also revealed that the SN FC in the dAI was increased following the treatment, despite the absence of AN-specific changes at baseline. The activity or FC in dAI has been reported to change after treatment for other diseases, such as schizophrenia  and major depressive disorder , suggesting that the SN FC change in the dAI observed in our study might not be specific to AN. The dAI is involved both in processing somatosensory inputs and decision-making in the initiation of behavior. This area integrates internal and external sensory information to coordinate brain network dynamics to initiate switches of the DMN and FPN [68–70], leading to behavioral changes. Improved SN FC in the dAI might promote appropriate behavior in response to perceived physical or psychological discomfort, such as perception of hunger in AN, which could be associated with improvement of the symptoms of AN.
The right PI revealed higher FC to DMN in pre-AN patients than in HCs. The interaction between the DMN and PI might be associated with abnormal somatic sensation in patients with AN. The PI would be associated with pain and somatosensory processes [69, 71]. Patients with chronic low back pain showed high FC between the DMN and PI , which is regarded as a diseased phenomenon. Patients with AN also present bodily complaints frequently, so their high FC in the PI to the DMN might indicate the psychological modification of somatosensory information , which relates to their mind-induced somatic symptoms.
The vAI showed lower SN FC in pre-AN patients compared with that in HCs. These phenomena might be associated with impaired socio-emotional processing, such as emotional cognition and empathy in patients with AN [74–77]. The vAI is connected to the rostral ACC , which plays an important role in socio-emotional processing in the SN [20, 69, 78, 79]. Abnormal function in the vAI has been reported in other diseases involving socio-emotional function, including bipolar disorder  and bronchial asthma with depression . SN FC of the rACC was lower in pre-AN patients compared with that in HCs, and was negatively correlated with interpersonal distrust. These findings have important implications for social maladjustment in patients with AN. The rACC is involved in empathy [82–84], and dysfunction in this region is believed to be related to maladaptive emotional processing and interpersonal stress [85, 86]. Hypofunction of the rACC was reported in several previous resting-state neuroimaging studies of patients with AN [21, 24, 87].
The current study showed that FPN FC in the AG was higher in pre-AN patients than in HCs and was positively correlated with TAS-20 DDF. The FPN is associated with executive cognition control [17–19], which is reported to be excessive in patients with AN from a clinical perspective [27, 88–90]. Patients with AN have been found to exhibit predominant FPN activity during set shifting and cognitive flexibility [88, 91], which may reflect that top-down cognitive control is dominant [89, 90, 92]. This excessive functioning might be an impediment to expression of fluid feelings. Previous resting-state network studies in both patients with AN and participants who had recovered from AN reported higher FPN FC in the AG [23, 27]. In the current study, post-AN patients showed no significant improvement. This neural function was not changed by the treatment, potentially indicating a neural trait in patients with AN.
Taken together, our findings indicated that DMN FC in the RSC, which is involved in self-reference and coping, showed significant changes with treatment, suggesting that this element is more “improvable.” This may have occurred because, among the various elements of our psychotherapeutic inpatient treatment, the promotion of introspection was reflected by improvements in neurological functioning. It is generally considered that treatments focused on improvable function are more likely to be effective. This view suggests that currently used treatments (i.e., enhanced cognitive behavior therapy, Maudsley model therapy, and focal psychodynamic psychotherapy) contain elements of introspection, compensate for weak functions, and have credible therapeutic effects [6, 93, 94], and should be noted when future treatment is revised.
First, the small sample size is a potential limitation of the current study, which may affect the generalizability of the results. Second, because we adopted an integrated treatment regimen involving nourishment, psychotherapy, and medical treatment, we were unable to attribute the observed effects to a specific elemental therapy. Psychotherapy, as well as changes in body weight and medication use, may have affected the change in FC. Third, many patients were mildly relieved by treatment, but had not fully recovered at the time of discharge. Fourth, we were unable to analyze how the neural change with treatment influenced the clinical course after discharge. Future studies with a larger sample size may be needed to investigate the improvement in RSN FC with therapy, differences in RSN FC depending on the subtypes or clinical characteristics, and the influence of treatment on long-term clinical consequences.
We acquired rsfMRI from patients with AN before and after undergoing integrated inpatient treatment. We analyzed RSNs of interest using ICA. Post-AN patients exhibited higher DMN FCs in the RSC and the hippocampus than pre-AN patients. These results might be related to the improvement of self-referential function and progress of introspection induced by the integrated treatment. Post-AN patients exhibited higher SN FC in the dAI than pre-AN patients. The increase in FC following treatment might promote appropriate coping with discomfort in emotion and sensation. The RSN FCs in AN-specific ROIs (the right PI in DMN, vAI/rACC in SN and AG in FPN), except RSC in DMN, did not show significant changes between before and after treatment. These phenomena might reflect trait neural pathology regarding abnormal somatic perception, difficulty in socio-emotional processing, and excessive cognitive control/difficulty in describing feelings in patients with AN. The current findings help to elucidate pathology and treatment effects in RSN in patients with AN, which may play a critical role in setting targets for future treatment.
S1 Table. An example of behavioral limitation.
We thank all of the participants, the nursing staff who engaged in treatment, and the radiological technician.
- 1. American Psychiatric Association APAD-, Force. T. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th edn2013:[xliv, 947pp.].
- 2. Jenkins PE , O’Connor H. Discerning thoughts from feelings: the cognitive-affective division in eating disorders. Eat Disord. 2012;20(2):144–58. Epub 2012/03/01. pmid:22364345.
- 3. Oldershaw A, Hambrook D, Stahl D, Tchanturia K, Treasure J, Schmidt U. The socio-emotional processing stream in Anorexia Nervosa. Neurosci Biobehav Rev. 2011;35(3):970–88. Epub 2010/11/13. pmid:21070808.
- 4. Schmidt U, Treasure J. Anorexia nervosa: valued and visible. A cognitive-interpersonal maintenance model and its implications for research and practice. The British journal of clinical psychology. 2006;45(Pt 3):343–66. Epub 2006/12/07. pmid:17147101.
- 5. Rawal A, Park RJ, Williams JM. Rumination, experiential avoidance, and dysfunctional thinking in eating disorders. Behaviour research and therapy. 2010;48(9):851–9. Epub 2010/07/06. pmid:20598670; PubMed PubMed Central PMCID: PMC2923742.
- 6. Fairburn CG. Cognitive Behavior Therapy and Eating Disorders. New York: The Guilford Press; 2008.
- 7. Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, et al. Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America. 2010;107(10):4734–9. Epub 2010/02/24. pmid:20176931; PubMed PubMed Central PMCID: PMC2842060.
- 8. Greicius MD, Supekar K, Menon V, Dougherty RF. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex. 2009;19(1):72–8. Epub 2008/04/12. pmid:18403396; PubMed Central PMCID: PMC2605172.
- 9. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, et al. Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences of the United States of America. 2006;103(37):13848–53. Epub 2006/09/02. pmid:16945915; PubMed Central PMCID: PMC1564249.
- 10. Gusnard DA, Raichle ME. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci. 2001;2(10):685–94. Epub 2001/10/05. pmid:11584306.
- 11. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008;1124:1–38. Epub 2008/04/11. pmid:18400922.
- 12. Kober H, Barrett LF, Joseph J, Bliss-Moreau E, Lindquist K, Wager TD. Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies. NeuroImage. 2008;42(2):998–1031. Epub 2008/06/27. pmid:18579414; PubMed Central PMCID: PMC2752702.
- 13. Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain’s default network. Neuron. 2010;65(4):550–62. Epub 2010/03/02. pmid:20188659; PubMed Central PMCID: PMC2848443.
- 14. Soto D, Theodoraki M, Paz-Alonso PM. How the human brain introspects about one’s own episodes of cognitive control. Cortex. 2018;107:110–20. Epub 2017/12/05. pmid:29198443.
- 15. Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST, Macrae CN. Wandering minds: the default network and stimulus-independent thought. Science. 2007;315(5810):393–5. Epub 2007/01/20. pmid:17234951; PubMed Central PMCID: PMC1821121.
- 16. Dosenbach NU, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RA, et al. Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(26):11073–8. Epub 2007/06/20. pmid:17576922; PubMed Central PMCID: PMC1904171.
- 17. Beaty RE, Cortes RA, Zeitlen DC, Weinberger AB, Green AE. Functional Realignment of Frontoparietal Subnetworks during Divergent Creative Thinking. Cereb Cortex. 2021. Epub 2021/04/26. pmid:33895837.
- 18. Spreng RN, Stevens WD, Chamberlain JP, Gilmore AW, Schacter DL. Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. NeuroImage. 2010;53(1):303–17. Epub 2010/07/06. pmid:20600998; PubMed Central PMCID: PMC2914129.
- 19. Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3(3):201–15. Epub 2002/05/08. pmid:11994752.
- 20. Uddin LQ. Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci. 2015;16(1):55–61. Epub 2014/11/20. pmid:25406711.
- 21. Gaudio S, Olivo G, Beomonte Zobel B, Schioth HB. Altered cerebellar-insular-parietal-cingular subnetwork in adolescents in the earliest stages of anorexia nervosa: a network-based statistic analysis. Transl Psychiatry. 2018;8(1):127. Epub 2018/07/08. pmid:29980676; PubMed Central PMCID: PMC6035187.
- 22. Phillipou A, Abel LA, Castle DJ, Hughes ME, Nibbs RG, Gurvich C, et al. Resting state functional connectivity in anorexia nervosa. Psychiatry Res Neuroimaging. 2016;251:45–52. Epub 2016/04/26. pmid:27111812.
- 23. Boehm I, Geisler D, Tam F, King JA, Ritschel F, Seidel M, et al. Partially restored resting-state functional connectivity in women recovered from anorexia nervosa. J Psychiatry Neurosci. 2016;41(6):377–85. Epub 2016/10/22. pmid:27045551; PubMed Central PMCID: PMC5082508.
- 24. Gaudio S, Piervincenzi C, Beomonte Zobel B, Romana Montecchi F, Riva G, Carducci F, et al. Altered resting state functional connectivity of anterior cingulate cortex in drug naive adolescents at the earliest stages of anorexia nervosa. Sci Rep. 2015;5:10818. Epub 2015/06/05. pmid:26043139; PubMed Central PMCID: PMC4455287.
- 25. McFadden KL, Tregellas JR, Shott ME, Frank GK. Reduced salience and default mode network activity in women with anorexia nervosa. J Psychiatry Neurosci. 2014;39(3):178–88. Epub 2013/11/28. pmid:24280181; PubMed Central PMCID: PMC3997603.
- 26. Cowdrey FA, Filippini N, Park RJ, Smith SM, McCabe C. Increased resting state functional connectivity in the default mode network in recovered anorexia nervosa. Human brain mapping. 2014;35(2):483–91. Epub 2012/10/04. pmid:23033154.
- 27. Boehm I, Geisler D, King JA, Ritschel F, Seidel M, Deza Araujo Y, et al. Increased resting state functional connectivity in the fronto-parietal and default mode network in anorexia nervosa. Front Behav Neurosci. 2014;8:346. Epub 2014/10/18. pmid:25324749; PubMed Central PMCID: PMC4183185.
- 28. Uniacke B, Wang Y, Biezonski D, Sussman T, Lee S, Posner J, et al. Resting-state connectivity within and across neural circuits in anorexia nervosa. Brain Behav. 2019;9(1):e01205. Epub 2018/12/28. pmid:30590873; PubMed Central PMCID: PMC6373651.
- 29. Cha J, Ide JS, Bowman FD, Simpson HB, Posner J, Steinglass JE. Abnormal reward circuitry in anorexia nervosa: A longitudinal, multimodal MRI study. Human brain mapping. 2016;37(11):3835–46. Epub 2016/06/09. pmid:27273474; PubMed Central PMCID: PMC5448464.
- 30. Amemiya N, Takii M, Hata T, Morita C, Takakura S, Oshikiri K, et al. The outcome of Japanese anorexia nervosa patients treated with an inpatient therapy in an internal medicine unit. Eating and weight disorders: EWD. 2011. Epub 2011/10/15. pmid:21997338.
- 31. Zung WW. A Self-Rating Depression Scale. Arch Gen Psychiatry. 1965;12:63–70. Epub 1965/01/01. pmid:14221692.
- 32. Garner DM, Olmstead MP, Polivy J. Development and validation of a multidimensional eating disorder inventory for anorexia nervosa and bulimia. International Journal of Eating Disorders. 1983;2(2):15–34. https://doi.org/10.1002/1098-108x(198321)2:2<15::aid-eat2260020203>3.0.co;2-6.
- 33. Bagby RM, Parker JD, Taylor GJ. The twenty-item Toronto Alexithymia Scale—I. Item selection and cross-validation of the factor structure. J Psychosom Res. 1994;38(1):23–32. Epub 1994/01/01. pmid:8126686.
- 34. Bagby RM, Taylor GJ, Parker JD. The Twenty-item Toronto Alexithymia Scale—II. Convergent, discriminant, and concurrent validity. J Psychosom Res. 1994;38(1):33–40. Epub 1994/01/01. pmid:8126688.
- 35. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34(4):537–41. Epub 1995/10/01. pmid:8524021.
- 36. Trojsi F, Di Nardo F, D’Alvano G, Caiazzo G, Passaniti C, Mangione A, et al. Resting state fMRI analysis of pseudobulbar affect in Amyotrophic Lateral Sclerosis (ALS): motor dysfunction of emotional expression. Brain imaging and behavior. 2022. Epub 2022/11/13. pmid:36370302.
- 37. Tang F, Li L, Peng D, Yu J, Xin H, Tang X, et al. Abnormal static and dynamic functional network connectivity in stable chronic obstructive pulmonary disease. Front Aging Neurosci. 2022;14:1009232. Epub 2022/11/04. pmid:36325191; PubMed Central PMCID: PMC9618865.
- 38. Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18(2):192–205. Epub 1994/03/01. pmid:8126267.
- 39. Calhoun VD, Adali T, Pearlson GD, Pekar JJ. A method for making group inferences from functional MRI data using independent component analysis. Human brain mapping. 2001;14(3):140–51. Epub 2001/09/18. pmid:11559959.
- 40. Li YO, Adali T, Calhoun VD. A feature-selective independent component analysis method for functional MRI. Int J Biomed Imaging. 2007;2007:15635. Epub 2008/02/22. pmid:18288254; PubMed Central PMCID: PMC2233814.
- 41. Bell AJ, Sejnowski TJ. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995;7(6):1129–59. Epub 1995/11/01. pmid:7584893.
- 42. Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex. 2012;22(1):158–65. Epub 2011/05/28. pmid:21616982; PubMed Central PMCID: PMC3236795.
- 43. Matthew Brett J-LA, Romain Valabregue, Jean-Baptiste Poline, editor Region of interest analysis using an SPM toolbox. the 8th International Conference on Functional Mapping of the Human Brain; 2002 June 2–6; Sendai, Japan.
- 44. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.
- 45. Kaboodvand N, Backman L, Nyberg L, Salami A. The retrosplenial cortex: A memory gateway between the cortical default mode network and the medial temporal lobe. Human brain mapping. 2018;39(5):2020–34. Epub 2018/01/25. pmid:29363256.
- 46. Vann SD, Aggleton JP, Maguire EA. What does the retrosplenial cortex do? Nat Rev Neurosci. 2009;10(11):792–802. Epub 2009/10/09. pmid:19812579.
- 47. Corcoran KA, Yamawaki N, Leaderbrand K, Radulovic J. Role of retrosplenial cortex in processing stress-related context memories. Behav Neurosci. 2018;132(5):388–95. Epub 2018/06/08. pmid:29878804; PubMed Central PMCID: PMC6188831.
- 48. Cato MA, Crosson B, Gokcay D, Soltysik D, Wierenga C, Gopinath K, et al. Processing words with emotional connotation: an FMRI study of time course and laterality in rostral frontal and retrosplenial cortices. J Cogn Neurosci. 2004;16(2):167–77. Epub 2004/04/08. pmid:15068589.
- 49. Piefke M, Weiss PH, Zilles K, Markowitsch HJ, Fink GR. Differential remoteness and emotional tone modulate the neural correlates of autobiographical memory. Brain. 2003;126(Pt 3):650–68. Epub 2003/02/05. pmid:12566286.
- 50. Oddo S, Lux S, Weiss PH, Schwab A, Welzer H, Markowitsch HJ, et al. Specific role of medial prefrontal cortex in retrieving recent autobiographical memories: an fMRI study of young female subjects. Cortex. 2010;46(1):29–39. Epub 2008/12/17. pmid:19084220.
- 51. Mitelman SA, Shihabuddin L, Brickman AM, Hazlett EA, Buchsbaum MS. Volume of the cingulate and outcome in schizophrenia. Schizophr Res. 2005;72(2–3):91–108. Epub 2004/11/25. pmid:15560955.
- 52. Nugent AC, Milham MP, Bain EE, Mah L, Cannon DM, Marrett S, et al. Cortical abnormalities in bipolar disorder investigated with MRI and voxel-based morphometry. NeuroImage. 2006;30(2):485–97. Epub 2005/11/01. pmid:16256376.
- 53. Sartory G, Cwik J, Knuppertz H, Schurholt B, Lebens M, Seitz RJ, et al. In search of the trauma memory: a meta-analysis of functional neuroimaging studies of symptom provocation in posttraumatic stress disorder (PTSD). PloS one. 2013;8(3):e58150. Epub 2013/03/29. pmid:23536785; PubMed Central PMCID: PMC3607590.
- 54. Liberzon I, Taylor SF, Amdur R, Jung TD, Chamberlain KR, Minoshima S, et al. Brain activation in PTSD in response to trauma-related stimuli. Biological psychiatry. 1999;45(7):817–26. Epub 1999/04/15. pmid:10202568.
- 55. Zhang R-T, Yang Z-Y, Wang Y-M, Wang Y, Yang T-X, Cheung EFC, et al. Affective forecasting in individuals with social anhedonia: The role of social components in anticipated emotion, prospection and neural activation. Schizophr Res. 2020;215:322–9. Epub 2019/10/11. pmid:31611042.
- 56. Yang Z-Y, Zhang R-T, Li Y, Wang Y, Wang Y-M, Wang S-K, et al. Functional connectivity of the default mode network is associated with prospection in schizophrenia patients and individuals with social anhedonia. Prog Neuropsychopharmacol Biol Psychiatry. 2019;92:412–20. Epub 2019/02/26. pmid:30822447.
- 57. Imperatori C, Farina B, Adenzato M, Valenti EM, Murgia C, Marca GD, et al. Default mode network alterations in individuals with high-trait-anxiety: An EEG functional connectivity study. J Affect Disord. 2019;246:611–8. Epub 2018/12/24. pmid:30605880.
- 58. Hogeveen J, Krug MK, Elliott MV, Solomon M. Insula-Retrosplenial Cortex Overconnectivity Increases Internalizing via Reduced Insight in Autism. Biological psychiatry. 2018;84(4):287–94. Epub 2018/01/31. pmid:29523413.
- 59. Shpaner M, Kelly C, Lieberman G, Perelman H, Davis M, Keefe FJ, et al. Unlearning chronic pain: A randomized controlled trial to investigate changes in intrinsic brain connectivity following Cognitive Behavioral Therapy. Neuroimage Clin. 2014;5:365–76. Epub 2014/01/01. pmid:26958466; PubMed Central PMCID: PMC4749849.
- 60. Fujino M, Ueda Y, Mizuhara H, Saiki J, Nomura M. Open monitoring meditation reduces the involvement of brain regions related to memory function. Sci Rep. 2018;8(1):9968. Epub 2018/07/04. pmid:29967435; PubMed Central PMCID: PMC6028418.
- 61. Huijbers W, Pennartz CM, Cabeza R, Daselaar SM. The hippocampus is coupled with the default network during memory retrieval but not during memory encoding. PloS one. 2011;6(4):e17463. Epub 2011/04/16. pmid:21494597; PubMed Central PMCID: PMC3073934.
- 62. Fanselow MS, Dong HW. Are the dorsal and ventral hippocampus functionally distinct structures? Neuron. 2010;65(1):7–19. Epub 2010/02/16. pmid:20152109; PubMed Central PMCID: PMC2822727.
- 63. Barsaglini A, Sartori G, Benetti S, Pettersson-Yeo W, Mechelli A. The effects of psychotherapy on brain function: a systematic and critical review. Prog Neurobiol. 2014;114:1–14. Epub 2013/11/06. pmid:24189360.
- 64. Goldapple K, Segal Z, Garson C, Lau M, Bieling P, Kennedy S, et al. Modulation of cortical-limbic pathways in major depression: treatment-specific effects of cognitive behavior therapy. Arch Gen Psychiatry. 2004;61(1):34–41. Epub 2004/01/07. pmid:14706942.
- 65. Peres JF, Newberg AB, Mercante JP, Simao M, Albuquerque VE, Peres MJ, et al. Cerebral blood flow changes during retrieval of traumatic memories before and after psychotherapy: a SPECT study. Psychological medicine. 2007;37(10):1481–91. Epub 2007/02/10. pmid:17288648.
- 66. He H, Yang M, Duan M, Chen X, Lai Y, Xia Y, et al. Music Intervention Leads to Increased Insular Connectivity and Improved Clinical Symptoms in Schizophrenia. Front Neurosci. 2017;11:744. Epub 2018/02/08. pmid:29410607; PubMed Central PMCID: PMC5787137.
- 67. Delaveau P, Jabourian M, Lemogne C, Guionnet S, Bergouignan L, Fossati P. Brain effects of antidepressants in major depression: a meta-analysis of emotional processing studies. J Affect Disord. 2011;130(1–2):66–74. Epub 2010/10/30. pmid:21030092.
- 68. Uddin LQ, Nomi JS, Hebert-Seropian B, Ghaziri J, Boucher O. Structure and Function of the Human Insula. J Clin Neurophysiol. 2017;34(4):300–6. Epub 2017/06/24. pmid:28644199; PubMed Central PMCID: PMC6032992.
- 69. Chang LJ, Yarkoni T, Khaw MW, Sanfey AG. Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cereb Cortex. 2013;23(3):739–49. Epub 2012/03/23. pmid:22437053; PubMed Central PMCID: PMC3563343.
- 70. Deen B, Pitskel NB, Pelphrey KA. Three systems of insular functional connectivity identified with cluster analysis. Cereb Cortex. 2011;21(7):1498–506. Epub 2010/11/26. pmid:21097516; PubMed Central PMCID: PMC3116731.
- 71. Craig AD. How do you feel? Interoception: the sense of the physiological condition of the body. Nat Rev Neurosci. 2002;3(8):655–66. Epub 2002/08/03. pmid:12154366.
- 72. Loggia ML, Kim J, Gollub RL, Vangel MG, Kirsch I, Kong J, et al. Default mode network connectivity encodes clinical pain: an arterial spin labeling study. Pain. 2013;154(1):24–33. Epub 2012/11/01. pmid:23111164; PubMed Central PMCID: PMC3534957.
- 73. Wentz E, Gillberg IC, Gillberg C, Rastam M. Ten-year follow-up of adolescent-onset anorexia nervosa: physical health and neurodevelopment. Dev Med Child Neurol. 2000;42(5):328–33. Epub 2000/06/16. pmid:10855653.
- 74. Nalbant K, Kalayci BM, Akdemir D, Akgul S, Kanbur N. Emotion regulation, emotion recognition, and empathy in adolescents with anorexia nervosa. Eating and weight disorders: EWD. 2019;24(5):825–34. Epub 2019/09/02. pmid:31473987.
- 75. Morris R, Bramham J, Smith E, Tchanturia K. Empathy and social functioning in anorexia nervosa before and after recovery. Cogn Neuropsychiatry. 2014;19(1):47–57. Epub 2013/05/24. pmid:23697879.
- 76. Cserjesi R, Vermeulen N, Lenard L, Luminet O. Reduced capacity in automatic processing of facial expression in restrictive anorexia nervosa and obesity. Psychiatry research. 2011;188(2):253–7. Epub 2011/01/07. pmid:21208661.
- 77. Kucharska-Pietura K, Nikolaou V, Masiak M, Treasure J. The recognition of emotion in the faces and voice of anorexia nervosa. Int J Eat Disord. 2004;35(1):42–7. Epub 2004/01/06. pmid:14705156.
- 78. Kelly C, Toro R, Di Martino A, Cox CL, Bellec P, Castellanos FX, et al. A convergent functional architecture of the insula emerges across imaging modalities. NeuroImage. 2012;61(4):1129–42. Epub 2012/03/24. pmid:22440648; PubMed Central PMCID: PMC3376229.
- 79. Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB. A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct Funct. 2010;214(5–6):519–34. Epub 2010/06/01. pmid:20512376; PubMed Central PMCID: PMC4801482.
- 80. Ellard KK, Gosai AK, Felicione JM, Peters AT, Shea CV, Sylvia LG, et al. Deficits in frontoparietal activation and anterior insula functional connectivity during regulation of cognitive-affective interference in bipolar disorder. Bipolar Disord. 2019;21(3):244–58. Epub 2018/12/20. pmid:30565822; PubMed Central PMCID: PMC6504612.
- 81. Zhang Y, Yang Y, Bian R, Yin Y, Hou Z, Yue Y, et al. Abnormal Functional Connectivity of Ventral Anterior Insula in Asthmatic Patients with Depression. Neural Plast. 2017;2017:7838035. Epub 2017/07/07. pmid:28680706; PubMed Central PMCID: PMC5478859.
- 82. Tolomeo S, Christmas D, Jentzsch I, Johnston B, Sprengelmeyer R, Matthews K, et al. A causal role for the anterior mid-cingulate cortex in negative affect and cognitive control. Brain. 2016;139(Pt 6):1844–54. Epub 2016/05/18. pmid:27190027.
- 83. Etkin A, Buchel C, Gross JJ. The neural bases of emotion regulation. Nat Rev Neurosci. 2015;16(11):693–700. Epub 2015/10/21. pmid:26481098.
- 84. Moriguchi Y, Decety J, Ohnishi T, Maeda M, Mori T, Nemoto K, et al. Empathy and judging other’s pain: an fMRI study of alexithymia. Cereb Cortex. 2007;17(9):2223–34. Epub 2006/12/08. pmid:17150987.
- 85. Lane RD. Neural substrates of implicit and explicit emotional processes: a unifying framework for psychosomatic medicine. Psychosom Med. 2008;70(2):214–31. Epub 2008/02/08. pmid:18256335.
- 86. Mulders PC, van Eijndhoven PF, Schene AH, Beckmann CF, Tendolkar I. Resting-state functional connectivity in major depressive disorder: A review. Neurosci Biobehav Rev. 2015;56:330–44. Epub 2015/08/04. pmid:26234819.
- 87. Kojima S, Nagai N, Nakabeppu Y, Muranaga T, Deguchi D, Nakajo M, et al. Comparison of regional cerebral blood flow in patients with anorexia nervosa before and after weight gain. Psychiatry research. 2005;140(3):251–8. Epub 2005/11/18. pmid:16288853.
- 88. Kaye WH, Wierenga CE, Bailer UF, Simmons AN, Bischoff-Grethe A. Nothing tastes as good as skinny feels: the neurobiology of anorexia nervosa. Trends Neurosci. 2013;36(2):110–20. Epub 2013/01/22. pmid:23333342; PubMed Central PMCID: PMC3880159.
- 89. Gaudio S, Wiemerslage L, Brooks SJ, Schioth HB. A systematic review of resting-state functional-MRI studies in anorexia nervosa: Evidence for functional connectivity impairment in cognitive control and visuospatial and body-signal integration. Neurosci Biobehav Rev. 2016;71:578–89. Epub 2016/10/30. pmid:27725172.
- 90. Pignatti R, Bernasconi V. Personality, clinical features, and test instructions can affect executive functions in Eating Disorders. Eat Behav. 2013;14(2):233–6. Epub 2013/04/06. pmid:23557828.
- 91. Zastrow A, Kaiser S, Stippich C, Walther S, Herzog W, Tchanturia K, et al. Neural correlates of impaired cognitive-behavioral flexibility in anorexia nervosa. Am J Psychiatry. 2009;166(5):608–16. Epub 2009/02/19. pmid:19223435.
- 92. Ritschel F, Geisler D, King JA, Bernardoni F, Seidel M, Boehm I, et al. Neural correlates of altered feedback learning in women recovered from anorexia nervosa. Sci Rep. 2017;7(1):5421. Epub 2017/07/16. pmid:28710363; PubMed Central PMCID: PMC5511172.
- 93. Schmidt U, Magill N, Renwick B, Keyes A, Kenyon M, Dejong H, et al. The Maudsley Outpatient Study of Treatments for Anorexia Nervosa and Related Conditions (MOSAIC): Comparison of the Maudsley Model of Anorexia Nervosa Treatment for Adults (MANTRA) with specialist supportive clinical management (SSCM) in outpatients with broadly defined anorexia nervosa: A randomized controlled trial. J Consult Clin Psychol. 2015;83(4):796–807. Epub 2015/05/20. pmid:25984803.
- 94. Zipfel S, Wild B, Gross G, Friederich HC, Teufel M, Schellberg D, et al. Focal psychodynamic therapy, cognitive behaviour therapy, and optimised treatment as usual in outpatients with anorexia nervosa (ANTOP study): randomised controlled trial. Lancet. 2014;383(9912):127–37. Epub 2013/10/18. pmid:24131861.