Default mode network connectivity as a possible biomarker for emotional self- awareness improvement in borderline personality disorder

Default mode network connectivity as a possible biomarker for emotional self- awareness improvement in borderline personality disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Default mode network connectivity as a possible biomarker for emotional self- awareness improvement in borderline personality disorder Saba Amiri, Fatemeh Sadat Mirfazeli, Jordan Grafman, Mehrdad Eftekhar, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1609095/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Borderline personality disorder (BPD) is characterized by impairments in impulse control, social functioning along with a deficit in emotional awareness and empathy. In this study, nine BPD patiens filled out the demography, Interpersonal Reactive Index(IRI), Toronto Alexithymia Scale 20 (TAS 20), The Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST), and the Borderline Evaluation Severity over Time (BEST) questionnaire. Default mode network (DMN) connectivity was measured using resting-state fMRI before psychodynamic psychotherapy and then every four months for a year after initiating psychotherapy. BPD patients present with aberrant DMN connectivity compared to healthy controls. Over a year of psychotherapy, the BPD patients showed both neuroimaging changes (decreasing nodal degree connection in the dorsal anterior cingulate cortex and increasing in other cingulate cortex regions) and behavioral improvement in their symptoms and substance use. There was also a significant positive association between the decreased nodal degree in regions of the cingulate gyrus and a decrease in the score of the TAS-20 indicating difficulty in identifying feelings after psychotherapy. In BPD, there is altered connectivity within the DMN and disruption in self-processing and emotion regulation. Psychotherapy may modify the DMN connectivity and that modification is associated with positive changes in BPD behavioral symptoms. Default mode network connectivity resting-state fMRI borderline personality disorder Figures Figure 1 Figure 2 Figure 3 Highlights Patients with BPD presented with aberrant DMN connectivity compared to a matched healthy control group. After one year of psychodynamic psychotherapy patients with BPD showed Behavioral improvement in their symptoms After one year of psychodynamic psychotherapy patients with BPD showed neuroimaging changes (hypo-connectivity in dACC) along with an increase in emotional awareness. 1. Introduction Borderline personality disorder (BPD) is a severe mental illness with a relatively high prevalence among the population,1% and 10 to 12% in the clinical outpatient setting 1 . It is characterized by impairments in emotion regulation, impulse control, and interpersonal and social functioning 2 . They may also have difficulty in comprehending their own feelings also known as alexithymia 3 . People with BPDs also show deficits in mentalizing 4 and self-awareness 5 , two processes that give us the capacity to understand our inner world. Furthermore, individuals with BPD do not merely have problems understanding their own emotions but also understanding and communicating others’ emotions i.e. empathizing 6 . In patients with BPD with disturbed self-image, identity, and empathizing 6 , resting-state connectivity is abnormal 7 . For example, in the study of Wolf 7 , patients with BPD had decreased connectivity of the left inferior parietal lobule and the mid-left temporal cortex in the default mode network (DMN). The DMS's interaction within midline regions shapes our sense of self 8 . DMN connectivity is also associated with emotional awareness 9 . The prognosis of BPD with these neurobiological changes over time is variable. Externalizing symptoms like self-destructive behaviors, impulsive reactions, and aggression tend to decline over time 1011 while internalizing symptoms such as identity confusion and sense of emptiness, which are the primary sources of suffering in these patients, may persist throughout life 12 . As a consequence, BPD patients are high-utilizers of medical resources. There are no FDA-approved medications for BPD and research in this field is more limited than other psychiatric conditions with similar or even fewer morbidities 1314 . Given the lack of therapeutic medications, the leading treatment choice is psychotherapy. There are various psychodynamic approaches for BPD patients; as many as eight different therapies for the treatment of BPD have been demonstrated to be effective in randomized controlled trials 15 . The primary mechanism of change in all psychotherapeutic interventions is improving patients' communication with the external world 16 . Despite the modest efficacy of psychotherapeutic interventions, there is minimal evidence whether a baseline evaluation of BPD can predict patient response to psychotherapeutic or medical interventions 1718 . In addition, research on the impact of BPD psychodynamic psychotherapy on default mode network functioning, empathetic behavior, and emotional awareness is lacking 619 . Below we report on default mode network connectivity patterns in patients with BPD compared to healthy control and their alteration after one year of psychodynamic psychotherapy. We also explored the association of this alteration with improvements in clinical symptoms, emotional awareness, and empathy. We hypothesized that psychotherapy would improve default mode network dysfunction, and that improvement would be associated with improvement in emotional self-awareness (decrease in alexithymia) and emotional communication (empathy). 2. Methods And Material 2.1. Participants Thirteen patients with BPD referring to clinics and psychiatric wards of Iran University of medical sciences were recruited. The diagnosis was based on a Structured Clinical Interview for Diagnostic-II (SCID-II) by trained examiners. Patients younger than 18-years-old, or older than 90-years-old were excluded as were patients with a major neurological disorder such as epilepsy, traumatic brain injury, comorbidity with antisocial personality disorder, substance use disorder during the year of study, alcohol or cannabis intoxication, major mood disorder, psychotic disorder, education lower than high school. To be enrolled in the study, patients were required to be stable on their medications for at least one month before recruiting. Participants entered the study after they were fully informed about the one-year duration and the research purpose and completed an informed consent. Four participants dropped out during one year of psychotherapy and they were excluded from the analyses. Nine healthy participants younger than 18-years-old, or older than 90-years-old were excluded as were patients with a major neurological disorder such as epilepsy, traumatic brain injury, comorbidity with antisocial personality disorder, substance use disorder during the year of study, alcohol or cannabis intoxication, major mood disorder, psychotic disorder, education lower than high school. All the research was approved by the ethical committee of the Iran University of Medical Sciences (Ethical code: IR.IUMS.REC.1398.872) and informed consent was taken from all the participants. All methods were performed in accordance with the relevant guidelines and regulations of Helsinki. 2.2. Instruments 2.2.1. Structured Clinical Interview for Diagnostic-I (SCID I) This is a semi-structured interview for examination of major axis I psychiatric disorders based on DSM IV criteria. Its reliability and validity in the Persian translation has been established and it’s test-retest reliability is fair to good 2021222324 . 2.2.2. Structured Clinical Interview for Diagnostic II (SCID II) The Structured Clinical Interview for Diagnostic-II (SCID-II), carried out as a semi-clinical structured interview, is conducted to diagnose personality disorders based on the DSM IV. The SCID-II shows adequate interrater reliability (from .48 to .98) and good reliability for dimensional diagnosis (from .90 to .98) and internal consistency (.71-.98). The SCID-II questionnaire was translated into Persian but its psychometric investigation is somewhat limited. In one study, the Persion SCID II test-retest reliability was reported at 0.87 25 . 2.2.3. The Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) This scale was first developed to evaluate a wide range of substance use and consequent problems in primary care patients. The ASSIST items were considered easy to answer and were found to be reliable and feasible to administer in an international study. The test-retest reliability coefficients ranged from 0.58 to 0.9. The reliability range for different categories of substances averaged 0.61 for sedatives to 0.78 for opioids 2627 . 2.2.4. Borderline evaluation of severity over time questionnaire (BEST) This is a self-report measure that assesses the change and severity of BPD, such as thoughts, feelings, and negative actions over time. It includes 15 items and three subscales on the Likert-like Range 28 . Its reliability and validity have been studied in Persian 29 . 2.2.5. Interpersonal Reactive Index (IRI) The IRI is a self-report questionnaire. It assesses four dimensions of empathy, and each subscale (empathic concern, perspective-taking, personal distress, and fantasy) is made up of 7 items. Participants rate how much an item will describe them on a 5-point Likert scale. (does not describe me well = 0 to represent me very well = 4). The maximum and minimum total scores on this questionnaire are 28 and 0, respectively. This questionnaire has shown relatively good psychometrics with high internal consistency (Alfa Cronbach of 0.68 to 0.79 (Davis, 1983 ), good reliability for both cognitive (alpha = 0.654) and emotional empathy (alpha = 0.767). Cronbach's Alpha of each subscale ranges between 0.7 to 0.77 (Davis, 1994). Test-retest reliability was reported within 0.62 to 0.8 303132 . This questionnaire has been translated into Persian and it’s psychometric properties have been studied extensively 33 . 2.2.6. Toronto Alexithymia Scale-20 (TAS-20) This questionnaire evaluates four dimensions of emotional awareness, including difficulty identifying the feeling, difficulty describing feelings, and externally oriented thinking. Its validity and reliability have been studied by Bagby and et al 34 and has shown fairly good reliability and validity in Persian 353637 . 2.3. Psychotherapy Once weekly, patients had a session of therapeutic session emphasizing the Transference Focused Psychotherapy approach. The content of the session was written by the therapist every week. The patient's progression through the year was noted and analyzed. Core concepts of transference and countertransference, defense mechanisms, and signs of emotional communication were highlighted. Each therapist had an individual supervisor. In addition, monthly group supervisory sessions were held to work through the dynamics of the sessions and feelings of being part of a study. 2.4. Procedure Participants were recruited by convenience sampling among patients referred to clinics and psychiatric wards of the Iran University of Medical Sciences. Those who meet inclusion criteria based on the SCID II interview entered the study. After completing the informed consent, participants filled out the demographic questionnaire, IRI, TAS 20, ASSIST, BEST questionnaire. Then their default mode network connectivity was measured using resting-state fMRI before initiating psychodynamic psychotherapy. After starting psychodynamic psychotherapy, DMN connections were assessed 3 more times with each assessment separated by approximately 4 months (so that including their baseline assessment, the total number of fMRI assessments for each individual was 4). Finally, at the conclusion of their therapy patients again completed the IRI, TAS 20, ASSIST, the BEST questionnaire to monitor any possible changes. 2.5. fMRI acquisition Multimodal MRI data were collected in a Siemens magnetom Prisma 3T MRI scanner. The resting-state functional MRI images covering the whole brain were obtained with an echo-planar imaging sequence with the following parameters: 240 volumes (8 min and 6 s), axial slices = 32 slices with 3.5 mm thickness, repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle (FA) = = 90°, voxel size: 3.1×3.1×3.5 mm, the field of view = 200×200 mm, and matrix size = of 64 × 64. T1-weighted structural images were acquired for co-registration of functional images using a sagittal 3D-magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence: TR 1800 ms, TE = 3.53 ms, inversion time(TI) = 1100 ms, FA = 7°, FOV = 256 × 256 mm 2 , matrix size = 256 × 256, slice thickness = 1 mm, and scan time = 4min and 12 s. 2.6. fMRI Analysis The analyses consist of five sequential steps that included pre-processing, extracting DMN FC matrix (FCM) based on the automated anatomical labeling (AAL) atlas, thresholding, and binary FCM, constructing binary graph network from binary FCM and extracting graph-theoretical features, and finally comparison and statistical analyses. 2.6.1. Preprocessing For each subject, Preprocessing of the rs-fMRI data was carried out using statistical parametric mapping (SPM12) and the data processing assistant for resting-state fMRI (DPARSF) toolbox version 4.5 38 . Briefly, the following steps were carried out: 1) removing the first 10 volumes of the 240 volumes to allow for magnetization equilibrium; 2) Skull stripping was performed on both functional and structural images to remove non-brain tissue before co-registration of T1 images and functional images for better registration of T1 image to functional space; 3) slice-timing correction; 4) correcting for head movements, which required the images to be realigned with a six-parameter (rigid body) linear transformation. Individual structural images were co-registered to mean functional images; 5) segmentation of T1-weighted images into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF); 6) regressing out of 27 nuisance covariates, including signals from WM and, CSF, global signals, and Friston 24 motion parameters; 7) Spatial normalization was done to the standard template Montreal Neurological Institute (MNI) space; 8) Spatial smoothening with a Gaussian kernel of 6 mm full-width at half-maximum (FWHM_); and 9) Subsequently, a temporal band pass filter (0.01–0.01Hz) was performed to reduce the influence of low-frequency drift and high frequency respiratory and cardiac noise. 2.6.2. Brain functional connectivity matrix (FCM) and Graph construction For analysis of FC, the seed regions of the default mode network(DMN) were chosen based on a priori knowledge 3940 from the AAL atlas 41 with the DMN regions shown in Table 1 . The time series for each region were extracted and then on each pair, the Pearson correlation was used to obtain a correlation matrix for each participant. Based on the correlation matrices, we constructed a weighted brain graph or weighted functional connectivity matrix by using a set of sparsity thresholds ranging from 5–40% with a step of 1% (5 ≤ T ≤ 40). The sparsity threshold represented the proportion of the present connections to the maximum possible connections within the network. This approach included assigning labels 1 to D% (density) of the strongest connections in each network and 0 to other connections. 42 . For group comparison, unlike the absolute threshold, the use of proportional thresholds ensures that the network of each group have the same number of nodes and edges 42 . This makes more meaningful comparisons between the two groups. We described FC with a network density of 5–40%. The Range of 5–40% was chosen for interpretation, because, according to previous reports, this range is in overall consistency with the biological background of the brain functional networks 4344 . Table 1 Regions of interest default mode network (DMN). DMN Regions (or nodes) Abbreviation Left and right Superior frontal gyrus, orbital part ORBsub.L, ORBsub.R Left and right Middle frontal gyrus MFG.L, MFG.R Left and right Inferior frontal gyrus, orbital part ORBinf.L, ORBinf.R Left and right Superior frontal gyrus, medial SFGmed.L, SFGmed.R Left and right Superior orbital frontal gyrus, medial ORBsupmed.L,ORBsupmed.R Left and right Anterior cingulate and Paracingulate gyri ACG.L, ACG.R Left and right Median cingulate and Paracingulate gyri DCG.L, DCG.R Left and right Posterior cingulate gyrus PCG.L, PCG.R Left and right Hippocampus HIP.L, HIP.R Left and right Parahippocampal gyrus PHG.L, PHG.R Left and right Inferior parietal lobule IPL.L, IPL.R Left and right Angular gyrus ANG.L, ANG.R Left and right Precuneus PCUN.L, PCUN.R Left and right Middle temporal gyrus MTG.L, MTG.R Left and right Temporal gyrus pole: middle temporal TPOmid.L, TPOmid.R Left and right Inferior temporal gyrus ITG.L, ITG.R 2.6.3. Graph-theoretical measures Centrality metrics can determine the importance of each node in a brain network, which makes them appropriate measures to capture the complexity of functional connectivity. Among these metrics, the nodal degree is the most popular measure of centrality since it is directly related to functional connectivity 454647 . Furthermore, the nodal degree is shown to have a high correlation with other centrality metrics (betweenness centrality, clustering coefficient, node neighbor's degree, and closeness centrality) 45484950 . We calculated the ‘ nodal degree ’ in DMN regions and compared patients with BPD with the healthy control group. The ‘ nodal degree ’ of each node equals the total number of edges that are connected to a node 51 46 . where N is the number of all nodes in the network, aij is the connection value between a pair of nodes (i and j), with aij = 1 when a connection between (i, j) exists, and aij = 0 unless otherwise. 2.6.4. Statistical analyses A nonparametric permutation test with 10000 resamples was used to evaluate the significance of differences in degree between the HC and BPD groups. The nonparametric permutation test is used to determine whether a measured effect is genuine or is a statistical anomaly due to the randomness associated with the selection of the sample 52 . Permutation testing for controlling the nominal type I error is considered acceptable 52 . We also used a non-parametric permutation test to assess the significance of the differences between groups (reported as p-values) and to determine the 95% confidence intervals 53 . 2.7. Association of nodal degree results with clinical measurements To determine the relationship between nodal degree results and clinical variables, Pearson correlation coefficients were calculated using SPSS 25 (SPSS Inc; Chicago, Illinois). The clinical variables included the empathy, assist, TAS, and best measures. Also, we used paired t -tests to evaluate differences between pre-and post- psychotherapy clinical evaluations. 3. Results 3.1. DMN alternation in BPD during psychotherapy compared to the HC 3.1.1. Baseline We first compared the HC and BPD groups at baseline and found that the nodal degree in left and right ACG in the BPD group was less than in the HC group (P < 0.05) (Fig. 1 .A). Furthermore, the nodal degree in the right DCG was greater in the BPD (baseline) group compared to the HC group (P < 0.05) In other regions of the DMN, no significant difference was found between the HC and BPD (baseline) group. 3.1.2. Four months’ post-psychotherapy onset The nodal degree was significantly greater in the BPD groups 4 months after psychotherapy, (Phase1) compared to the HC group, in the right PCUN and DCG (P < 0.05). Also, the nodal degree in the left inferior ORB was lesser in the BPD (phase1) group compared to the HC group (P < 0.05) (Fig. 1 .B). 3.1.3. Eight months’ post-psychotherapy onset Compared with the HC group, the BPD group, 8 months after psychotherapy (Phase2), showed a significantly greater nodal degree in the right DCG (P < 0.05). Furthermore, the nodal degree in the right ACG and left ANG was less in the BPD (phase2) group compared to the HC group (P < 0.05) (Fig. 1 .C). 3.1.4. Twelve months’ post-psychotherapy onset Comparing HC and BPD groups 12 months after the onset of psychotherapy (phase3), the nodal degree in the right ITG in the BPD group was greater than in the HC group (P < 0.05) (Fig. 1 .D). In other regions of the DMN, no significant difference was found between the HC and BPD (phase3) groups. 3.2. DMN alternation in BPD during psychotherapy compared to the BPD in baseline In the DMN, the nodal degree was significantly greater in the right ACG in the BPD group 4 months after psychotherapy compared to their baseline (P < 0.05) (Fig. 2 .A). In contrast, the nodal degree was significantly less in left ORBinf and right PCG in DMN (P < 0.05) (Fig. 2 .A). In the DMN, no significant difference was found between BPD (8 months after psychotherapy) and BPD (baseline). Comparing the baseline to 12 months after psychotherapy in the BPD group, the nodal degree in right DCG and right PCG in the BPD group 12 months after psychotherapy was less than that seen at baseline (P < 0.05). Furthermore, the nodal degree in the right ACG was greater in the BPD group 12 months after psychotherapy compared to baseline (P < 0.05) (Fig. 2 .B). 3.3. Alternation nodal degree of ACG, DCG in BPD pre-post psychotherapy Figure-3. A shows Nodal degree of ACG, DCG, and PCG pre and post psychotherapy. Three major points emerged: 1) nodal degree of DCG and PCG after 12-month psychotherapy were significantly decreased in the BPD group. 2)In contrast nodal degree of ACG in the BPD group after 12-month psychotherapy was significantly increased.3) after 12-month psychotherapy, nodal degree pattern in ACG, DCG, and PCG in BPD group similar to the pattern of nodal degree these regions in the HC group. Figure-3. B shows a schematic brain view of ACG, DCG, and PCG. 3.4. statistical analysis on clinical measurements We found a significant positive association between the decreased nodal degree of DCG and decrease in the score of difficulty identify feeling subscale of the TAS-20 (R = 0.801, after FDR correction at q < 0.01) after psychotherapy. Results of the dependent (paired) sample t-tests indicated that there were significant differences in the score of Assist, the score of Best, and the score of TAS (difficulty identify feeling subscale) between pre and post psychotherapy (Table 2 ). Mean values in post psychotherapy for Assist, Best and TAS decreased significantly (P value < 0.05). Table 2 Differences score of Assist, Best ,TAS and empathy (pre-post) parameters Pre(baseline) Mean ± std Post (12 months) Mean ± std Mean change(post-pre) P value Assist 13.67 ± 13.51 10 ± 10.13 -3.67 0.021 * Best 44.00 ± 10.05 35.89 ± 9.49 -8.11 0.008 * TAS(difficulty identifying feeling subscale) 24.22 ± 5.14 18.89 ± 6.22 -5.33 0.012* TAS(difficulty describing feeling subscale) 15.89 ± 4.14 13.56 ± 3.36 -2.33 0.264 TAS(attention) 23.00 ± 7.35 20.56 ± 3.71 -2.44 0.458 Total TAS 63.11 ± 13.22 53 ± 8.57 -10.11 0.087 EC (empathic concern) 16.00 ± 4.12 17.11 ± 4.80 1.11 0.481 PT(perspective talking) 14.11 ± 1.9 14.44 ± 3.88 0.33 0.784 PD(personal distress) 15.78 ± 5.54 17.33 ± 4.53 1.56 0.391 F(fantasy) 14.78 ± 4.66 15.56 ± 6.13 0.78 0.417 *P value < 0.05 is considered statistically significant 4. Discussion Our study showed that patients with BPD present with aberrant DMN connectivity compared to a matched healthy control group. However, after one year of psychodynamic psychotherapy, they showed neuroimaging changes (hyperconnectivity in ACC and hypo-connectivity in dACC (DCG)) that were associated with behavioral improvement in their symptoms and decreased substance use. We also found that the decrease in dACC connectivity was associated with BPD patient improvement in identifying their feeling. The BPD patients demonstrated aberrant connectivity including hyperconnectivity in the dACC and hypoconnectivity in the ACC in their DMN before starting psychodynamic psychotherapy. This finding is in line with previous studies that showed abnormal connections in the DMN in patients with BPD 5455 . Previous studies showed structural abnormalities in GM in the DMN and frontolimbic circuit 54 and disturbed activity in the regions of the midline core and the dorsal subsystem of the DMN in patients with BPD. These abnormalities are thought to reflect interpersonal emotional communication and emotion regulation difficulties in BPD 55 . More specifically, amygdala hyperactivity along with ACC hypoactivity has been proposed as the neural mechanism of emotion dysregulation in negative emotion processes in BPD 5657 . ACC abnormal activity suggests a dysfunction in frontolimibic circuitry which suggests a reduced capacity of patients with BPD to effectively activate their PFC during emotional situations leading to hyperlimbic activity and hyperarousal in these situations 585960616263 . The DMN is also related to emotional self-referential processing 626364 , which is markedly affected in BPD. Accordingly, we also observed hyperconnectivity in the dACC. The dorsal portion of the dACC is considered critical for salience detection, attention regulation and cognitive control 65666768697071727374 . Furthermore, in emotionally charged situations, there is an interaction between attention and emotion in the dACC 7576 . Experimental tasks that direct attention towards emotion engage the dACC 777879 . Greater awareness of one's own emotional experiences is associated with greater recruitment of the dACC during emotional arousal. This finding might reflect greater attention to processing emotional information 80 . However, in patients with BPD, we found hyperconnectivity of dACC in resting state. This finding, given previous reports on BPD patient ruminations, suggests increased attention to negative social information and enhanced self-referential processing (e.g. retrieval of negative memories of interpersonal events) during the resting state in patients with BPD. Our results, however, showed that psychotherapy may help to regulate BPD dysfunctional behavior (leading to an increase in ACC and decrease in dACC connectivity). BPD patients showed a reduction in symptom severity over time and a decrease in substance abuse. They also showed less difficulty in identifying their feelings and emotions. These findings were associated with a reduction in dACC hyperactivity. It is possible that psychotherapy creates a secure atmosphere to effectively process traumatic interpersonal events and emotional regulation, resulting in a DMN resting state approaching normal. Our study has some notable caveats. Due to the long course of psychotherapy, we had BPD patient dropouts and our small sample size requires replication with a larger sample size. Furthermore, our exclusion criteria led to the omission of more severe BPD patients. In conclusion, in BPD, there is altered connectivity within DMN and disruption in self-processing and emotion regulation. Psychotherapy may not only alleviate behavioral dysfunction but also normalizes connectivity within the DMN. Declarations Acknowledgments The authors thank all research participants and the Iranian National Brain Mapping Laboratory (NBML), Tehran, Iran, for providing the data acquisition service. Author Contributions Statement S.A. and FS.M. wrote the main manuscript text and contributed to the conception and design of the experiment, and analysis of the data. S.N., H.M., M.E., and J.G. contributed to the idea, design, and development of the experiment and editing of the main manuscript text. N.K. and M.M. collected the data. Data availability statement Data can be made available upon reasonable request. References Ellison, W. D., Rosenstein, L. K., Morgan, T. A. & Zimmerman, M. Community and clinical epidemiology of borderline personality disorder. Psychiatric Clinics 41 , 561–573 (2018). Edition, F. %J A. P. A. Diagnostic and statistical manual of mental disorders. 21 , (2013). Nemiah, J. C., Sifneos, P. E. %J P. & psychosomatics. Psychosomatic illness: a problem in communication. 18 , 154–160 (1970). Bateman, A. W. & Fonagy, P. %J J. of personality disorders. Mentalization-based treatment of BPD. 18 , 36–51 (2004). Semerari, A., Carcione, A., Dimaggio, G., Nicolò, G. & Procacci, M. %J P. R. Understanding minds: Different functions and different disorders? The contribution of psychotherapy research. 17 , 106–119 (2007). Harari, H., Shamay-Tsoory, S. G., Ravid, M. & Levkovitz, Y. %J P. research. Double dissociation between cognitive and affective empathy in borderline personality disorder. 175 , 277–279 (2010). Wolf, R. C. et al. Aberrant connectivity of resting-state networks in borderline personality disorder. 36 , 402 (2011). Qin, P. & Northoff, G. %J N. How is our self related to midline regions and the default-mode network? 57 , 1221–1233 (2011). Smith, R. et al. Resting state functional connectivity correlates of emotional awareness. NeuroImage 159 , 99–106 (2017). Kaess, M., Brunner, R. & Chanen, A. %J P. Borderline personality disorder in adolescence. 134 , 782–793 (2014). Morgan, T. A., Chelminski, I., Young, D., Dalrymple, K. & Zimmerman, M. %J J. of psychiatric research. Differences between older and younger adults with borderline personality disorder on clinical presentation and impairment. 47 , 1507–1513 (2013). Fonagy, P. et al. ESCAP Expert Article: Borderline personality disorder in adolescence: An expert research review with implications for clinical practice. 24 , 1307–1320 (2015). Lieb, K., Völlm, B., Rücker, G., Timmer, A. & Stoffers, J. M. %J T. B. J. of P. Pharmacotherapy for borderline personality disorder: Cochrane systematic review of randomised trials. 196 , 4–12 (2010). Ripoll, L. H., Triebwasser, J. & Siever, L. J. %J T. T. I. J. of N. Evidence-based pharmacotherapy for personality disorders. 14 , 1257–1288 (2011). Leichsenring, F., Leibing, E., Kruse, J., New, A. S. & Leweke, F. %J T. L. Borderline personality disorder. 377 , 74–84 (2011). Fonagy, P., Luyten, P. & Allison, E. %J J. of personality disorders. Epistemic petrification and the restoration of epistemic trust: A new conceptualization of borderline personality disorder and its psychosocial treatment. 29 , 575–609 (2015). Stanley, B., Perez-Rodriguez, M. M., Labouliere, C. & Roose, S. %J J. of personality disorders. A neuroscience-oriented research approach to borderline personality disorder. 32 , 784–822 (2018). Perez, D. L. et al. Frontolimbic neural circuit changes in emotional processing and inhibitory control associated with clinical improvement following transference-focused psychotherapy in borderline personality disorder. 70 , 51–61 (2016). Abbass, A. A. et al. Review of psychodynamic psychotherapy neuroimaging studies. 83 , 142–147 (2014). Mohammadi, M.-R., Amini, H., Kaviani, H. & Shabani, A. %J I. J. P. Structured Clinical Interview for DSM-IV (SCID): Persian translation and cultural adaptation. 2 , 46 (2007). First, M. B. %J B. R. D. Structured clinical interview for DSM-IV axis I disorders. (1997). First, M. B., Spitzer, R. L., Gibbon, M. & Williams, J. B. W. User’s guide for the Structured clinical interview for DSM-IV axis I disorders SCID-I: clinician version . (American Psychiatric Pub, 1997). First, M. B., Spitzer, R. L., Gibbon, M. & Williams, J. B. W. Structured clinical interview for DSM-IV-TR axis I disorders, research version, patient edition . (SCID-I/P New York, NY, 2002). SHARIFI, V. et al. RELIABILITY AND FEASIBILITY OF THE PERSIAN VERSION OF THE STRUCTURED DIAGNOSTIC INTERVIEW FOR DSM-IV (SCID). 6 , (2004). Maffei, C. et al. Interrater reliability and internal consistency of the structured clinical interview for DSM-IV axis II personality disorders (SCID-II), version 2.0. 11 , 279–284 (1997). Group, W. H. O. A. W. %J A. The alcohol, smoking and substance involvement screening test (ASSIST): development, reliability and feasibility. 97 , 1183–1194 (2002). Humeniuk, R. et al. Validation of the alcohol, smoking and substance involvement screening test (ASSIST). (2008). Pfohl, B. et al. Reliability and Validity of the Borderline Evaluation of Severity Over Time (Best): A Self-Rated Scale to Measure Severity and Change in Persons With Borderline Personality Disorder. Journal of Personality Disorders 23 , 281–293 (2009). Azizi, M. R. et al. Validity and reliability of Persian translation of the Borderline Evaluation of Severity over Time (BEST) questionnaire. 33 , 133 (2019). Davis, M. H. %J P. & Bulletin, S. P. Empathic concern and the muscular dystrophy telethon: Empathy as a multidimensional construct. 9 , 223–229 (1983). Davis, M. H. %J J. of personality & psychology, social. Measuring individual differences in empathy: Evidence for a multidimensional approach. 44 , 113 (1983). Davis, M. H. %J J. of personality. The effects of dispositional empathy on emotional reactions and helping: A multidimensional approach. 51 , 167–184 (1983). Ghorbani, N., Watson, P. J., Lotfi, S., Chen Cognition, Z. %J I. & Personality. Moral affects, empathy, and integrative self-knowledge in Iran. 34 , 39–56 (2014). Bagby, R. M., Parker, J. D. A. & Taylor, G. J. %J J. of psychosomatic research. The twenty-item Toronto Alexithymia Scale—I. Item selection and cross-validation of the factor structure. 38 , 23–32 (1994). Bagby, M., Taylor, G. J., Ryan, D. %J P. & Psychosomatics. Toronto Alexithymia Scale: Relationship with personality and psychopathology measures. 45 , 207–215 (1986). Besharat, M. A. %J I. J. of M. S. Psychometric characteristics of Persian version of the Toronto alexithymia scale-20 in clinical and non-clinical samples. 33 , 1–6 (2008). Besharat, M. A. %J P. reports. Reliability and factorial validity of a Farsi version of the 20-item Toronto Alexithymia Scale with a sample of Iranian students. 101 , 209–220 (2007). Chao-Gan, Y. & Yu-Feng, Z. DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in systems neuroscience 4 , (2010). Chiang, S., Stern, J. M., Engel Jr, J., Levin, H. S. & Haneef, Z. Differences in graph theory functional connectivity in left and right temporal lobe epilepsy. Epilepsy research 108 , 1770–1781 (2014). Ting, C.-M., Ombao, H., Samdin, S. B. & Salleh, S.-H. Estimating dynamic connectivity states in fMRI using regime-switching factor models. IEEE transactions on medical imaging 37 , 1011–1023 (2017). Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15 , 273–289 (2002). van den Heuvel, M. P. et al. Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations. Neuroimage 152 , 437–449 (2017). Fornito, A., Zalesky, A. & Bullmore, E. T. Network scaling effects in graph analytic studies of human resting-state fMRI data. Frontiers in Systems Neuroscience 4 , 1–16 (2010). Fornito, A. et al. Genetic influences on cost-efficient organization of human cortical functional networks. Journal of Neuroscience 31 , 3261–3270 (2011). Oldham, S. et al. Consistency and differences between centrality measures across distinct classes of networks. PloS one 14 , e0220061 (2019). Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10 , 186 (2009). Amiri, S., Arbabi, M., Kazemi, K., Parvaresh-Rizi, M. & Mirbagheri, M. M. Characterization of brain functional connectivity in treatment-resistant depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry 111 , 110346 (2021). Rueda, D. F., Calle, E. & Marzo, J. L. Robustness comparison of 15 real telecommunication networks: Structural and centrality measurements. Journal of Network and Systems Management 25 , 269–289 (2017). Amiri, S. et al. Graph theory application with functional connectivity to distinguish left from right temporal lobe epilepsy. Epilepsy Research 167 , 106449 (2020). Amiri, S. et al. Brain functional connectivity in individuals with psychogenic nonepileptic seizures (PNES): An application of graph theory. Epilepsy & Behavior 114 , 107565 (2021). Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature reviews neuroscience 10 , 186 (2009). Nichols, T. E. & Holmes, A. P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human brain mapping 15 , 1–25 (2002). Mijalkov, M. et al. BRAPH: A graph theory software for the analysis of brain connectivity. PloS one 12 , e0178798 (2017). Yang, X., Hu, L., Zeng, J., Tan, Y. & Cheng, B. %J S. reports. Default mode network and frontolimbic gray matter abnormalities in patients with borderline personality disorder: a voxel-based meta-analysis. 6 , 1–10 (2016). Visintin, E. et al. Mapping the brain correlates of borderline personality disorder: a functional neuroimaging meta-analysis of resting state studies. 204 , 262–269 (2016). Baczkowski, B. M. et al. Deficient amygdala–prefrontal intrinsic connectivity after effortful emotion regulation in borderline personality disorder. 267 , 551–565 (2017). Silbersweig, D. et al. Failure of frontolimbic inhibitory function in the context of negative emotion in borderline personality disorder. 164 , 1832–1841 (2007). Kraus, N. & Chandrasekaran, B. %J N. reviews neuroscience. Music training for the development of auditory skills. 11 , 599–605 (2010). Ruocco, A. C., Amirthavasagam, S., Choi-Kain, L. W. & McMain, S. F. %J B. psychiatry. Neural correlates of negative emotionality in borderline personality disorder: an activation-likelihood-estimation meta-analysis. 73 , 153–160 (2013). Smoski, M. J. et al. Functional imaging of emotion reactivity in opiate-dependent borderline personality disorder. Personality Disorders: Theory, Research, and Treatment 2 , 230–241 (2011). Schulze, L., Schmahl, C. & Niedtfeld, I. %J B. psychiatry. Neural correlates of disturbed emotion processing in borderline personality disorder: a multimodal meta-analysis. 79 , 97–106 (2016). Andrews-Hanna, J. R., Smallwood, J. & Spreng, R. N. %J A. of the N. Y. A. of S. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. 1316 , 29 (2014). Kraus, A. et al. Script-driven imagery of self‐injurious behavior in patients with borderline personality disorder: A pilot FMRI study. 121 , 41–51 (2010). Andrews-Hanna, J. R., Saxe, R. & Yarkoni, T. %J N. Contributions of episodic retrieval and mentalizing to autobiographical thought: evidence from functional neuroimaging, resting-state connectivity, and fMRI meta-analyses. 91 , 324–335 (2014). Bush, G., Luu, P. & Posner, M. I. Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences 4 , 215–222 (2000). Weissman, D. H., Roberts, K. C., Visscher, K. M. & Woldorff, M. G. %J N. neuroscience. The neural bases of momentary lapses in attention. 9 , 971–978 (2006). Nee, D. E., Wager, T. D. & Jonides, J. Interference resolution: Insights from a meta-analysis of neuroimaging tasks. Cognitive, Affective, & Behavioral Neuroscience 7 , 1–17 (2007). Wager, T. D. & Smith, E. E. Neuroimaging studies of working memory. Cognitive, Affective, & Behavioral Neuroscience 3 , 255–274 (2003). Dosenbach, N. U. F. et al. A Core System for the Implementation of Task Sets. Neuron 50 , 799–812 (2006). Seeley, W. W. et al. Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control. 27 , 2349–2356 (2007). Petersen, S. E. & Posner, M. I. %J A. review of neuroscience. The attention system of the human brain: 20 years after. 35 , 73–89 (2012). Niendam, T. A. et al. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cognitive, Affective, & Behavioral Neuroscience 12 , 241–268 (2012). Menon, V. & Uddin, L. Q. Saliency, switching, attention and control: a network model of insula function. Brain Structure and Function 214 , 655–667 (2010). Menon, V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences 15 , 483–506 (2011). Fichtenholtz, H. M. et al. Emotion–attention network interactions during a visual oddball task. 20 , 67–80 (2004). Carlsson, K. et al. Fear and the amygdala: manipulation of awareness generates differential cerebral responses to phobic and fear-relevant (but nonfeared) stimuli. 4 , 340 (2004). Lane, R. D., Fink, G. R., Chau, P. M.-L. & Dolan, R. J. %J N. Neural activation during selective attention to subjective emotional responses. 8 , 3969–3972 (1997). Taylor, S. F., Phan, K. L., Decker, L. R. & Liberzon, I. %J N. Subjective rating of emotionally salient stimuli modulates neural activity. 18 , 650–659 (2003). Hutcherson, C. A. et al. Attention and emotion: does rating emotion alter neural responses to amusing and sad films? 27 , 656–668 (2005). Kim, D.-Y. & Lee, J.-H. Are posterior default-mode networks more robust than anterior default-mode networks? Evidence from resting-state fMRI data analysis. Neuroscience Letters 498 , 57–62 (2011). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1609095","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":105478286,"identity":"281cc860-7d04-4d11-b245-654785ad462d","order_by":0,"name":"Saba Amiri","email":"","orcid":"","institution":"Tehran University of Medical Sciences (TUMS)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Saba","middleName":"","lastName":"Amiri","suffix":""},{"id":105478287,"identity":"f9c9db6d-dde8-4a56-8df7-c920fcb4fc8f","order_by":1,"name":"Fatemeh Sadat Mirfazeli","email":"","orcid":"","institution":"Iran University of Medical Sciences(IUMS)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"Sadat","lastName":"Mirfazeli","suffix":""},{"id":105478288,"identity":"7f1fbfac-ab24-4545-a017-67b8137b1b63","order_by":2,"name":"Jordan Grafman","email":"","orcid":"","institution":"Northwestern University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jordan","middleName":"","lastName":"Grafman","suffix":""},{"id":105478289,"identity":"533e7977-c139-4074-89a1-6637520500a5","order_by":3,"name":"Mehrdad Eftekhar","email":"","orcid":"","institution":"Iran University of Medical Sciences(IUMS)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mehrdad","middleName":"","lastName":"Eftekhar","suffix":""},{"id":105478292,"identity":"756fd6f4-e7ab-4e11-922b-48bda84331c4","order_by":4,"name":"Nazila Karimzad","email":"","orcid":"","institution":"Iran University of Medical Sciences(IUMS)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nazila","middleName":"","lastName":"Karimzad","suffix":""},{"id":105478294,"identity":"338f14d1-1684-4e49-8377-c9a77bc234d2","order_by":5,"name":"Maryam Mohebbi","email":"","orcid":"","institution":"Iran University of Medical Sciences(IUMS)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Mohebbi","suffix":""},{"id":105478296,"identity":"6d51d901-1639-4a5f-9336-3883e26a37e1","order_by":6,"name":"Homa Mohammadsadeghi","email":"","orcid":"","institution":"Iran University of Medical Sciences(IUMS)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Homa","middleName":"","lastName":"Mohammadsadeghi","suffix":""},{"id":105478300,"identity":"0429b18f-72ca-4337-8091-686d2cac098a","order_by":7,"name":"Shabnam Nohesara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYFACHjiD8UECjJ2ATSUWLcwGJGthkyDKWebsvQcf/qi5J8/Af/ZYxcO2bfYM7IcfMDzcg1uLZc+5ZGOeY8WGDRJ5aTcS224nNvCkGTAkPMOtxeBGjpk0A1sCY4MEjxlIC9AXOUC/HMCrxfznj38J9g38Z8wKgFrsGfjfENRixsDblpDYwABkALUArSNky5kzxtK8fQnJbRI5xhIJ524ntkk8MziAV8vxHsOPP74l2PbznwEyym7b8/MnP3z4A48WOGBDZhCjYRSMglEwCkYBHgAAdrVOv5R7SsYAAAAASUVORK5CYII=","orcid":"","institution":"Iran University of Medical Sciences(IUMS)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shabnam","middleName":"","lastName":"Nohesara","suffix":""}],"badges":[],"createdAt":"2022-04-29 14:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1609095/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1609095/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21623297,"identity":"a7975681-f027-46c4-9808-8a969d6af707","added_by":"auto","created_at":"2022-05-18 16:15:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168809,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the nodal degree values of DMN regions between the HC and BPD groups, using the non-parametric permutation test.\u0026nbsp;Dark-blue points present the difference in nodal degree values between the healthy and BPD groups(BPD-HC), which lie within the confidence intervals presented by the light-blue zone. The actual difference value (dark-blue color points) is significant (\u0026lt;0.05) if it falls outside the confidence intervals (light-blue zone).\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1609095/v1/eb30fb1a45519a9585ec661e.jpg"},{"id":21623295,"identity":"3f0b2412-6b6f-4e27-ab8e-68abe3029321","added_by":"auto","created_at":"2022-05-18 16:15:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107316,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the nodal degree values of DMN regions between the BPD in baseline and BPD groups after, using the non-parametric permutation test.\u0026nbsp;Dark-blue points present the difference in nodal degree values between the BPD(baseline) and BPD groups after psychotherapy (BPD-BPD(baseline)), which lie within the confidence intervals presented by the light-blue zone. The actual difference value (dark-blue color points) is significant (\u0026lt;0.05) if it falls outside the confidence intervals (light-blue zone).\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1609095/v1/f0d2fb8960405242caeb24ca.jpg"},{"id":21623774,"identity":"b075681f-5dd6-45d9-a889-40466c719b4e","added_by":"auto","created_at":"2022-05-18 16:20:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171261,"visible":true,"origin":"","legend":"\u003cp\u003eNodal degree of ACG, DCG and PCG in BPD pre-post psychotherapy. A) Nodal degree of right DCG and right ACC in baseline BPD (red square) or pre psychotherapy, BPD after 12 months’ psychotherapy (green circle) and HC (blue star). B) ACG (red circle) increased nodal degree in post psychotherapy DCG and PCG (blue circle) decreased nodal degree in post psychotherapy\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1609095/v1/9038d436f0a11dd3f94a29b0.jpg"},{"id":23031453,"identity":"b35f1207-0a45-40f4-b9b3-c0edf6e5f91d","added_by":"auto","created_at":"2022-06-24 06:29:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":841683,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1609095/v1/d2617dbf-b827-4424-9daa-47b11e8e74ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Default mode network connectivity as a possible biomarker for emotional self- awareness improvement in borderline personality disorder","fulltext":[{"header":"Highlights ","content":"\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePatients with BPD presented with aberrant DMN connectivity compared to a matched healthy control group.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAfter one year of psychodynamic psychotherapy patients with BPD showed Behavioral improvement in their symptoms\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAfter one year of psychodynamic psychotherapy patients with BPD showed neuroimaging changes (hypo-connectivity in dACC) along with an increase in emotional awareness.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eBorderline personality disorder (BPD) is a severe mental illness with a relatively high prevalence among the population,1% and 10 to 12% in the clinical outpatient setting \u003csup\u003e1\u003c/sup\u003e. It is characterized by impairments in emotion regulation, impulse control, and interpersonal and social functioning \u003csup\u003e2\u003c/sup\u003e. They may also have difficulty in comprehending their own feelings also known as alexithymia \u003csup\u003e3\u003c/sup\u003e. People with BPDs also show deficits in mentalizing\u003csup\u003e4\u003c/sup\u003e and self-awareness \u003csup\u003e5\u003c/sup\u003e, two processes that give us the capacity to understand our inner world. Furthermore, individuals with BPD do not merely have problems understanding their own emotions but also understanding and communicating others\u0026rsquo; emotions i.e. empathizing \u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn patients with BPD with disturbed self-image, identity, and empathizing\u003csup\u003e6\u003c/sup\u003e, resting-state connectivity is abnormal \u003csup\u003e7\u003c/sup\u003e. For example, in the study of Wolf \u003csup\u003e7\u003c/sup\u003e, patients with BPD had decreased connectivity of the left inferior parietal lobule and the mid-left temporal cortex in the default mode network (DMN). The DMS's interaction within midline regions shapes our sense of self \u003csup\u003e8\u003c/sup\u003e. DMN connectivity is also associated with emotional awareness \u003csup\u003e9\u003c/sup\u003e. The prognosis of BPD with these neurobiological changes over time is variable. Externalizing symptoms like self-destructive behaviors, impulsive reactions, and aggression tend to decline over time \u003csup\u003e1011\u003c/sup\u003e while internalizing symptoms such as identity confusion and sense of emptiness, which are the primary sources of suffering in these patients, may persist throughout life\u003csup\u003e12\u003c/sup\u003e. As a consequence, BPD patients are high-utilizers of medical resources. There are no FDA-approved medications for BPD and research in this field is more limited than other psychiatric conditions with similar or even fewer morbidities \u003csup\u003e1314\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the lack of therapeutic medications, the leading treatment choice is psychotherapy. There are various psychodynamic approaches for BPD patients; as many as eight different therapies for the treatment of BPD have been demonstrated to be effective in randomized controlled trials \u003csup\u003e15\u003c/sup\u003e. The primary mechanism of change in all psychotherapeutic interventions is improving patients' communication with the external world \u003csup\u003e16\u003c/sup\u003e. Despite the modest efficacy of psychotherapeutic interventions, there is minimal evidence whether a baseline evaluation of BPD can predict patient response to psychotherapeutic or medical interventions \u003csup\u003e1718\u003c/sup\u003e. In addition, research on the impact of BPD psychodynamic psychotherapy on default mode network functioning, empathetic behavior, and emotional awareness is lacking \u003csup\u003e619\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBelow we report on default mode network connectivity patterns in patients with BPD compared to healthy control and their alteration after one year of psychodynamic psychotherapy. We also explored the association of this alteration with improvements in clinical symptoms, emotional awareness, and empathy. We hypothesized that psychotherapy would improve default mode network dysfunction, and that improvement would be associated with improvement in emotional self-awareness (decrease in alexithymia) and emotional communication (empathy).\u003c/p\u003e"},{"header":"2. Methods And Material","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Participants\u003c/h2\u003e\n \u003cp\u003eThirteen patients with BPD referring to clinics and psychiatric wards of Iran University of medical sciences were recruited. The diagnosis was based on a Structured Clinical Interview for Diagnostic-II (SCID-II) by trained examiners. Patients younger than 18-years-old, or older than 90-years-old were excluded as were patients with a major neurological disorder such as epilepsy, traumatic brain injury, comorbidity with antisocial personality disorder, substance use disorder during the year of study, alcohol or cannabis intoxication, major mood disorder, psychotic disorder, education lower than high school. To be enrolled in the study, patients were required to be stable on their medications for at least one month before recruiting. Participants entered the study after they were fully informed about the one-year duration and the research purpose and completed an informed consent. Four participants dropped out during one year of psychotherapy and they were excluded from the analyses. Nine healthy participants younger than 18-years-old, or older than 90-years-old were excluded as were patients with a major neurological disorder such as epilepsy, traumatic brain injury, comorbidity with antisocial personality disorder, substance use disorder during the year of study, alcohol or cannabis intoxication, major mood disorder, psychotic disorder, education lower than high school. All the research was approved by the ethical committee of the Iran University of Medical Sciences (Ethical code: IR.IUMS.REC.1398.872) and informed consent was taken from all the participants. All methods were performed in accordance with the relevant guidelines and regulations of Helsinki.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Instruments\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.2.1. Structured Clinical Interview for Diagnostic-I (SCID I)\u003c/h2\u003e\n \u003cp\u003eThis is a semi-structured interview for examination of major axis I psychiatric disorders based on DSM IV criteria. Its reliability and validity in the Persian translation has been established and it\u0026rsquo;s test-retest reliability is fair to good \u003csup\u003e2021222324\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.2.2. Structured Clinical Interview for Diagnostic II (SCID II)\u003c/h2\u003e\n \u003cp\u003eThe Structured Clinical Interview for Diagnostic-II (SCID-II), carried out as a semi-clinical structured interview, is conducted to diagnose personality disorders based on the DSM IV. The SCID-II shows adequate interrater reliability (from .48 to .98) and good reliability for dimensional diagnosis (from .90 to .98) and internal consistency (.71-.98). The SCID-II questionnaire was translated into Persian but its psychometric investigation is somewhat limited. In one study, the Persion SCID II test-retest reliability was reported at 0.87 \u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.2.3. The Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST)\u003c/h2\u003e\n \u003cp\u003eThis scale was first developed to evaluate a wide range of substance use and consequent problems in primary care patients. The ASSIST items were considered easy to answer and were found to be reliable and feasible to administer in an international study. The test-retest reliability coefficients ranged from 0.58 to 0.9. The reliability range for different categories of substances averaged 0.61 for sedatives to 0.78 for opioids\u003csup\u003e2627\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec8\"\u003e\n \u003ch2\u003e2.2.4. Borderline evaluation of severity over time questionnaire (BEST)\u003c/h2\u003e\n \u003cp\u003eThis is a self-report measure that assesses the change and severity of BPD, such as thoughts, feelings, and negative actions over time. It includes 15 items and three subscales on the Likert-like Range \u003csup\u003e28\u003c/sup\u003e. Its reliability and validity have been studied in Persian \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec9\"\u003e\n \u003ch2\u003e2.2.5. Interpersonal Reactive Index (IRI)\u003c/h2\u003e\n \u003cp\u003eThe IRI is a self-report questionnaire. It assesses four dimensions of empathy, and each subscale (empathic concern, perspective-taking, personal distress, and fantasy) is made up of 7 items.\u003c/p\u003e\n \u003cp\u003eParticipants rate how much an item will describe them on a 5-point Likert scale. (does not describe me well\u0026thinsp;=\u0026thinsp;0 to represent me very well\u0026thinsp;=\u0026thinsp;4). The maximum and minimum total scores on this questionnaire are 28 and 0, respectively. This questionnaire has shown relatively good psychometrics with high internal consistency (Alfa Cronbach of 0.68 to 0.79 (Davis, \u003cspan class=\"CitationRef\"\u003e1983\u003c/span\u003e), good reliability for both cognitive (alpha\u0026thinsp;=\u0026thinsp;0.654) and emotional empathy (alpha\u0026thinsp;=\u0026thinsp;0.767). Cronbach\u0026apos;s Alpha of each subscale ranges between 0.7 to 0.77 (Davis, 1994). Test-retest reliability was reported within 0.62 to 0.8 \u003csup\u003e303132\u003c/sup\u003e. This questionnaire has been translated into Persian and it\u0026rsquo;s psychometric properties have been studied extensively \u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec10\"\u003e\n \u003ch2\u003e2.2.6. Toronto Alexithymia Scale-20 (TAS-20)\u003c/h2\u003e\n \u003cp\u003eThis questionnaire evaluates four dimensions of emotional awareness, including difficulty identifying the feeling, difficulty describing feelings, and externally oriented thinking. Its validity and reliability have been studied by Bagby and et al \u003csup\u003e34\u003c/sup\u003e and has shown fairly good reliability and validity in Persian \u003csup\u003e353637\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003e2.3. Psychotherapy\u003c/h2\u003e\n \u003cp\u003eOnce weekly, patients had a session of therapeutic session emphasizing the Transference Focused Psychotherapy approach. The content of the session was written by the therapist every week. The patient\u0026apos;s progression through the year was noted and analyzed. Core concepts of transference and countertransference, defense mechanisms, and signs of emotional communication were highlighted. Each therapist had an individual supervisor. In addition, monthly group supervisory sessions were held to work through the dynamics of the sessions and feelings of being part of a study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003e2.4. Procedure\u003c/h2\u003e\n \u003cp\u003eParticipants were recruited by convenience sampling among patients referred to clinics and psychiatric wards of the Iran University of Medical Sciences. Those who meet inclusion criteria based on the SCID II interview entered the study. After completing the informed consent, participants filled out the demographic questionnaire, IRI, TAS 20, ASSIST, BEST questionnaire. Then their default mode network connectivity was measured using resting-state fMRI before initiating psychodynamic psychotherapy. After starting psychodynamic psychotherapy, DMN connections were assessed 3 more times with each assessment separated by approximately 4 months (so that including their baseline assessment, the total number of fMRI assessments for each individual was 4). Finally, at the conclusion of their therapy patients again completed the IRI, TAS 20, ASSIST, the BEST questionnaire to monitor any possible changes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003e2.5. fMRI acquisition\u003c/h2\u003e\n \u003cp\u003eMultimodal MRI data were collected in a Siemens magnetom Prisma 3T MRI scanner. The resting-state functional MRI images covering the whole brain were obtained with an echo-planar imaging sequence with the following parameters: 240 volumes (8 min and 6 s), axial slices\u0026thinsp;=\u0026thinsp;32 slices with 3.5 mm thickness, repetition time (TR)\u0026thinsp;=\u0026thinsp;2000 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;30 ms, flip angle (FA)\u0026thinsp;=\u0026thinsp;=\u0026thinsp;90\u0026deg;, voxel size: 3.1\u0026times;3.1\u0026times;3.5 mm, the field of view\u0026thinsp;=\u0026thinsp;200\u0026times;200 mm, and matrix size\u0026thinsp;=\u0026thinsp;of 64 \u0026times; 64. T1-weighted structural images were acquired for co-registration of functional images using a sagittal 3D-magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence: TR 1800 ms, TE\u0026thinsp;=\u0026thinsp;3.53 ms, inversion time(TI)\u0026thinsp;=\u0026thinsp;1100 ms, FA\u0026thinsp;=\u0026thinsp;7\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm\u003csup\u003e2\u003c/sup\u003e, matrix size\u0026thinsp;\u003cstrong\u003e=\u003c/strong\u003e\u0026thinsp;256 \u0026times; 256, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, and scan time\u0026thinsp;=\u0026thinsp;4min and 12 s.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003e2.6. fMRI Analysis\u003c/h2\u003e\n \u003cp\u003eThe analyses consist of five sequential steps that included pre-processing, extracting DMN FC matrix (FCM) based on the automated anatomical labeling (AAL) atlas, thresholding, and binary FCM, constructing binary graph network from binary FCM and extracting graph-theoretical features, and finally comparison and statistical analyses.\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec15\"\u003e\n \u003ch2\u003e2.6.1. Preprocessing\u003c/h2\u003e\n \u003cp\u003eFor each subject, Preprocessing of the rs-fMRI data was carried out using statistical parametric mapping (SPM12) and the data processing assistant for resting-state fMRI (DPARSF) toolbox version 4.5 \u003csup\u003e38\u003c/sup\u003e. Briefly, the following steps were carried out: 1) removing the first 10 volumes of the 240 volumes to allow for magnetization equilibrium; 2) Skull stripping was performed on both functional and structural images to remove non-brain tissue before co-registration of T1 images and functional images for better registration of T1 image to functional space; 3) slice-timing correction; 4) correcting for head movements, which required the images to be realigned with a six-parameter (rigid body) linear transformation. Individual structural images were co-registered to mean functional images; 5) segmentation of T1-weighted images into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF); 6) regressing out of 27 nuisance covariates, including signals from WM and, CSF, global signals, and Friston 24 motion parameters; 7) Spatial normalization was done to the standard template Montreal Neurological Institute (MNI) space; 8) Spatial smoothening with a Gaussian kernel of 6 mm full-width at half-maximum (FWHM_); and 9) Subsequently, a temporal band pass filter (0.01\u0026ndash;0.01Hz) was performed to reduce the influence of low-frequency drift and high frequency respiratory and cardiac noise.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec16\"\u003e\n \u003ch2\u003e2.6.2. Brain functional connectivity matrix (FCM) and Graph construction\u003c/h2\u003e\n \u003cp\u003eFor analysis of FC, the seed regions of the default mode network(DMN) were chosen based on a priori knowledge \u003csup\u003e3940\u003c/sup\u003e from the AAL atlas \u003csup\u003e41\u003c/sup\u003e with the DMN regions shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The time series for each region were extracted and then on each pair, the Pearson correlation was used to obtain a correlation matrix for each participant. Based on the correlation matrices, we constructed a weighted brain graph or weighted functional connectivity matrix by using a set of sparsity thresholds ranging from 5\u0026ndash;40% with a step of 1% (5\u0026thinsp;\u0026le;\u0026thinsp;T\u0026thinsp;\u0026le;\u0026thinsp;40). The sparsity threshold represented the proportion of the present connections to the maximum possible connections within the network. This approach included assigning labels 1 to D% (density) of the strongest connections in each network and 0 to other connections. \u003csup\u003e42\u003c/sup\u003e. For group comparison, unlike the absolute threshold, the use of proportional thresholds ensures that the network of each group have the same number of nodes and edges\u003csup\u003e42\u003c/sup\u003e. This makes more meaningful comparisons between the two groups. We described FC with a network density of 5\u0026ndash;40%. The Range of 5\u0026ndash;40% was chosen for interpretation, because, according to previous reports, this range is in overall consistency with the biological background of the brain functional networks \u003csup\u003e4344\u003c/sup\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegions of interest default mode network (DMN).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDMN Regions (or nodes)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Superior frontal gyrus, orbital part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eORBsub.L, ORBsub.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Middle frontal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMFG.L, MFG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Inferior frontal gyrus, orbital part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eORBinf.L, ORBinf.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Superior frontal gyrus, medial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSFGmed.L, SFGmed.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Superior orbital frontal gyrus, medial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eORBsupmed.L,ORBsupmed.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Anterior cingulate and Paracingulate gyri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACG.L, ACG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Median cingulate and Paracingulate gyri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDCG.L, DCG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Posterior cingulate gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCG.L, PCG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIP.L, HIP.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Parahippocampal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHG.L, PHG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPL.L, IPL.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Angular gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANG.L, ANG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Precuneus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCUN.L, PCUN.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Middle temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMTG.L, MTG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Temporal gyrus pole: middle temporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPOmid.L, TPOmid.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft and right Inferior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eITG.L, ITG.R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec17\"\u003e\n \u003ch2\u003e2.6.3. Graph-theoretical measures\u003c/h2\u003e\n \u003cp\u003eCentrality metrics can determine the importance of each node in a brain network, which makes them appropriate measures to capture the complexity of functional connectivity. Among these metrics, the nodal degree is the most popular measure of centrality since it is directly related to functional connectivity \u003csup\u003e454647\u003c/sup\u003e. Furthermore, the nodal degree is shown to have a high correlation with other centrality metrics (betweenness centrality, clustering coefficient, node neighbor\u0026apos;s degree, and closeness centrality) \u003csup\u003e45484950\u003c/sup\u003e. We calculated the \u0026lsquo;\u003cem\u003enodal degree\u003c/em\u003e\u0026rsquo; in DMN regions and compared patients with BPD with the healthy control group.\u003c/p\u003e\n \u003cp\u003eThe \u0026lsquo;\u003cem\u003enodal degree\u003c/em\u003e\u0026rsquo; of each node equals the total number of edges that are connected to a node \u003csup\u003e51 46\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere N is the number of all nodes in the network, aij is the connection value between a pair of nodes (i and j), with aij\u0026thinsp;=\u0026thinsp;1 when a connection between (i, j) exists, and aij\u0026thinsp;=\u0026thinsp;0 unless otherwise.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec18\"\u003e\n \u003ch2\u003e2.6.4. Statistical analyses\u003c/h2\u003e\n \u003cp\u003eA nonparametric permutation test with 10000 resamples was used to evaluate the significance of differences in \u003cem\u003edegree\u003c/em\u003e between the HC and BPD groups. The nonparametric permutation test is used to determine whether a measured effect is genuine or is a statistical anomaly due to the randomness associated with the selection of the sample \u003csup\u003e52\u003c/sup\u003e. Permutation testing for controlling the nominal type I error is considered acceptable \u003csup\u003e52\u003c/sup\u003e. We also used a non-parametric permutation test to assess the significance of the differences between groups (reported as p-values) and to determine the 95% confidence intervals \u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec19\"\u003e\n \u003ch2\u003e2.7. Association of nodal degree results with clinical measurements\u003c/h2\u003e\n \u003cp\u003eTo determine the relationship between nodal degree results and clinical variables, Pearson correlation coefficients were calculated using SPSS 25 (SPSS Inc; Chicago, Illinois). The clinical variables included the empathy, assist, TAS, and best measures. Also, we used paired \u003cem\u003et\u003c/em\u003e-tests to evaluate differences between pre-and post- psychotherapy clinical evaluations.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"Section2\" id=\"Sec21\"\u003e\n \u003ch2\u003e3.1. DMN alternation in BPD during psychotherapy compared to the HC\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec22\"\u003e\n \u003ch2\u003e3.1.1. Baseline\u003c/h2\u003e\n \u003cp\u003eWe first compared the HC and BPD groups at baseline and found that the nodal degree in left and right ACG in the BPD group was \u003cem\u003eless\u003c/em\u003e than in the HC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.A). Furthermore, the nodal degree in the right DCG was \u003cem\u003egreater\u003c/em\u003e in the BPD (baseline) group compared to the HC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) In other regions of the DMN, no significant difference was found between the HC and BPD (baseline) group.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec23\"\u003e\n \u003ch2\u003e3.1.2. Four months\u0026rsquo; post-psychotherapy onset\u003c/h2\u003e\n \u003cp\u003eThe nodal degree was significantly greater in the BPD groups 4 months after psychotherapy, (Phase1) compared to the HC group, in the right PCUN and DCG (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Also, the nodal degree in the left inferior ORB was lesser in the BPD (phase1) group compared to the HC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.B).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec24\"\u003e\n \u003ch2\u003e3.1.3. Eight months\u0026rsquo; post-psychotherapy onset\u003c/h2\u003e\n \u003cp\u003eCompared with the HC group, the BPD group, 8 months after psychotherapy (Phase2), showed a significantly greater nodal degree in the right DCG (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the nodal degree in the right ACG and left ANG was less in the BPD (phase2) group compared to the HC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.C).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec25\"\u003e\n \u003ch2\u003e3.1.4. Twelve months\u0026rsquo; post-psychotherapy onset\u003c/h2\u003e\n \u003cp\u003eComparing HC and BPD groups 12 months after the onset of psychotherapy (phase3), the nodal degree in the right ITG in the BPD group was greater than in the HC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.D). In other regions of the DMN, no significant difference was found between the HC and BPD (phase3) groups.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec26\"\u003e\n \u003ch2\u003e3.2. DMN alternation in BPD during psychotherapy compared to the BPD in baseline\u003c/h2\u003e\n \u003cp\u003eIn the DMN, the nodal degree was significantly greater in the right ACG in the BPD group 4 months after psychotherapy compared to their baseline (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.A). In contrast, the nodal degree was significantly less in left ORBinf and right PCG in DMN (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.A). In the DMN, no significant difference was found between BPD (8 months after psychotherapy) and BPD (baseline). Comparing the baseline to 12 months after psychotherapy in the BPD group, the nodal degree in right DCG and right PCG in the BPD group 12 months after psychotherapy was less than that seen at baseline (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the nodal degree in the right ACG was greater in the BPD group 12 months after psychotherapy compared to baseline (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.B).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec27\"\u003e\n \u003ch2\u003e3.3. Alternation nodal degree of ACG, DCG in BPD pre-post psychotherapy\u003c/h2\u003e\n \u003cp\u003eFigure-3. A shows Nodal degree of ACG, DCG, and PCG pre and post psychotherapy. Three major points emerged: 1) nodal degree of DCG and PCG after 12-month psychotherapy were significantly decreased in the BPD group. 2)In contrast nodal degree of ACG in the BPD group after 12-month psychotherapy was significantly increased.3) after 12-month psychotherapy, nodal degree pattern in ACG, DCG, and PCG in BPD group similar to the pattern of nodal degree these regions in the HC group. Figure-3. B shows a schematic brain view of ACG, DCG, and PCG.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec28\"\u003e\n \u003ch2\u003e3.4. statistical analysis on clinical measurements\u003c/h2\u003e\n \u003cp\u003eWe found a significant positive association between the decreased nodal degree of DCG and decrease in the score of difficulty identify feeling subscale of the TAS-20 (R\u0026thinsp;=\u0026thinsp;0.801, after FDR correction at q\u0026thinsp;\u0026lt;\u0026thinsp;0.01) after psychotherapy. Results of the dependent (paired) sample t-tests indicated that there were significant differences in the score of Assist, the score of Best, and the score of TAS (difficulty identify feeling subscale) between pre and post psychotherapy (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Mean values in post psychotherapy for Assist, Best and TAS decreased significantly (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferences score of Assist, Best ,TAS and empathy (pre-post)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eparameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre(baseline)\u003c/p\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;std\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost (12 months)\u003c/p\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;std\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean change(post-pre)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.67\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.89\u0026thinsp;\u0026plusmn;\u0026thinsp;9.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTAS(difficulty identifying feeling subscale)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.89\u0026thinsp;\u0026plusmn;\u0026thinsp;6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTAS(difficulty describing feeling subscale)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTAS(attention)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal TAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.11\u0026thinsp;\u0026plusmn;\u0026thinsp;13.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC (empathic concern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT(perspective talking)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD(personal distress)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(fantasy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e*P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered statistically significant\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study showed that patients with BPD present with aberrant DMN connectivity compared to a matched healthy control group. However, after one year of psychodynamic psychotherapy, they showed neuroimaging changes (hyperconnectivity in ACC and hypo-connectivity in dACC (DCG)) that were associated with behavioral improvement in their symptoms and decreased substance use. We also found that the decrease in dACC connectivity was associated with BPD patient improvement in identifying their feeling.\u003c/p\u003e \u003cp\u003eThe BPD patients demonstrated aberrant connectivity including hyperconnectivity in the dACC and hypoconnectivity in the ACC in their DMN before starting psychodynamic psychotherapy. This finding is in line with previous studies that showed abnormal connections in the DMN in patients with BPD \u003csup\u003e5455\u003c/sup\u003e. Previous studies showed structural abnormalities in GM in the DMN and frontolimbic circuit \u003csup\u003e54\u003c/sup\u003e and disturbed activity in the regions of the midline core and the dorsal subsystem of the DMN in patients with BPD. These abnormalities are thought to reflect interpersonal emotional communication and emotion regulation difficulties in BPD \u003csup\u003e55\u003c/sup\u003e. More specifically, amygdala hyperactivity along with ACC hypoactivity has been proposed as the neural mechanism of emotion dysregulation in negative emotion processes in BPD \u003csup\u003e5657\u003c/sup\u003e. ACC abnormal activity suggests a dysfunction in frontolimibic circuitry which suggests a reduced capacity of patients with BPD to effectively activate their PFC during emotional situations leading to hyperlimbic activity and hyperarousal in these situations\u003csup\u003e585960616263\u003c/sup\u003e. The DMN is also related to emotional self-referential processing \u003csup\u003e626364\u003c/sup\u003e, which is markedly affected in BPD. Accordingly, we also observed hyperconnectivity in the dACC. The dorsal portion of the dACC is considered critical for salience detection, attention regulation and cognitive control \u003csup\u003e65666768697071727374\u003c/sup\u003e. Furthermore, in emotionally charged situations, there is an interaction between attention and emotion in the dACC \u003csup\u003e7576\u003c/sup\u003e. Experimental tasks that direct attention towards emotion engage the dACC \u003csup\u003e777879\u003c/sup\u003e. Greater awareness of one's own emotional experiences is associated with greater recruitment of the dACC during emotional arousal. This finding might reflect greater attention to processing emotional information \u003csup\u003e80\u003c/sup\u003e. However, in patients with BPD, we found hyperconnectivity of dACC in resting state. This finding, given previous reports on BPD patient ruminations, suggests increased attention to negative social information and enhanced self-referential processing (e.g. retrieval of negative memories of interpersonal events) during the resting state in patients with BPD.\u003c/p\u003e \u003cp\u003eOur results, however, showed that psychotherapy may help to regulate BPD dysfunctional behavior (leading to an increase in ACC and decrease in dACC connectivity). BPD patients showed a reduction in symptom severity over time and a decrease in substance abuse. They also showed less difficulty in identifying their feelings and emotions. These findings were associated with a reduction in dACC hyperactivity. It is possible that psychotherapy creates a secure atmosphere to effectively process traumatic interpersonal events and emotional regulation, resulting in a DMN resting state approaching normal. Our study has some notable caveats. Due to the long course of psychotherapy, we had BPD patient dropouts and our small sample size requires replication with a larger sample size. Furthermore, our exclusion criteria led to the omission of more severe BPD patients.\u003c/p\u003e \u003cp\u003eIn conclusion, in BPD, there is altered connectivity within DMN and disruption in self-processing and emotion regulation. Psychotherapy may not only alleviate behavioral dysfunction but also normalizes connectivity within the DMN.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all research participants and the Iranian National Brain Mapping Laboratory (NBML), Tehran, Iran, for providing the data acquisition service.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.A. and FS.M. wrote the main manuscript text and contributed to the conception and design of the experiment, and analysis of the data. S.N., H.M., M.E., and J.G. contributed to the idea, design, and development of the experiment and editing of the main manuscript text. N.K. and M.M. collected the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData can be made available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEllison, W. D., Rosenstein, L. K., Morgan, T. A. \u0026amp; Zimmerman, M. Community and clinical epidemiology of borderline personality disorder. Psychiatric Clinics \u003cb\u003e41\u003c/b\u003e, 561\u0026ndash;573 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdition, F. %J A. P. A. Diagnostic and statistical manual of mental disorders. \u003cb\u003e21\u003c/b\u003e, (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNemiah, J. C., Sifneos, P. E. %J P. \u0026amp; psychosomatics. Psychosomatic illness: a problem in communication. \u003cb\u003e18\u003c/b\u003e, 154\u0026ndash;160 (1970).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBateman, A. W. \u0026amp; Fonagy, P. %J J. of personality disorders. Mentalization-based treatment of BPD. \u003cb\u003e18\u003c/b\u003e, 36\u0026ndash;51 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSemerari, A., Carcione, A., Dimaggio, G., Nicol\u0026ograve;, G. \u0026amp; Procacci, M. %J P. R. Understanding minds: Different functions and different disorders? The contribution of psychotherapy research. \u003cb\u003e17\u003c/b\u003e, 106\u0026ndash;119 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarari, H., Shamay-Tsoory, S. G., Ravid, M. \u0026amp; Levkovitz, Y. %J P. research. Double dissociation between cognitive and affective empathy in borderline personality disorder. \u003cb\u003e175\u003c/b\u003e, 277\u0026ndash;279 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf, R. C. \u003cem\u003eet al.\u003c/em\u003e Aberrant connectivity of resting-state networks in borderline personality disorder. \u003cb\u003e36\u003c/b\u003e, 402 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin, P. \u0026amp; Northoff, G. %J N. How is our self related to midline regions and the default-mode network? \u003cb\u003e57\u003c/b\u003e, 1221\u0026ndash;1233 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, R. \u003cem\u003eet al.\u003c/em\u003e Resting state functional connectivity correlates of emotional awareness. NeuroImage \u003cb\u003e159\u003c/b\u003e, 99\u0026ndash;106 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaess, M., Brunner, R. \u0026amp; Chanen, A. %J P. Borderline personality disorder in adolescence. \u003cb\u003e134\u003c/b\u003e, 782\u0026ndash;793 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgan, T. A., Chelminski, I., Young, D., Dalrymple, K. \u0026amp; Zimmerman, M. %J J. of psychiatric research. Differences between older and younger adults with borderline personality disorder on clinical presentation and impairment. \u003cb\u003e47\u003c/b\u003e, 1507\u0026ndash;1513 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFonagy, P. \u003cem\u003eet al.\u003c/em\u003e ESCAP Expert Article: Borderline personality disorder in adolescence: An expert research review with implications for clinical practice. \u003cb\u003e24\u003c/b\u003e, 1307\u0026ndash;1320 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLieb, K., V\u0026ouml;llm, B., R\u0026uuml;cker, G., Timmer, A. \u0026amp; Stoffers, J. M. %J T. B. J. of P. Pharmacotherapy for borderline personality disorder: Cochrane systematic review of randomised trials. \u003cb\u003e196\u003c/b\u003e, 4\u0026ndash;12 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRipoll, L. H., Triebwasser, J. \u0026amp; Siever, L. J. %J T. T. I. J. of N. Evidence-based pharmacotherapy for personality disorders. \u003cb\u003e14\u003c/b\u003e, 1257\u0026ndash;1288 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeichsenring, F., Leibing, E., Kruse, J., New, A. S. \u0026amp; Leweke, F. %J T. L. Borderline personality disorder. \u003cb\u003e377\u003c/b\u003e, 74\u0026ndash;84 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFonagy, P., Luyten, P. \u0026amp; Allison, E. %J J. of personality disorders. Epistemic petrification and the restoration of epistemic trust: A new conceptualization of borderline personality disorder and its psychosocial treatment. \u003cb\u003e29\u003c/b\u003e, 575\u0026ndash;609 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanley, B., Perez-Rodriguez, M. M., Labouliere, C. \u0026amp; Roose, S. %J J. of personality disorders. A neuroscience-oriented research approach to borderline personality disorder. \u003cb\u003e32\u003c/b\u003e, 784\u0026ndash;822 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez, D. L. \u003cem\u003eet al.\u003c/em\u003e Frontolimbic neural circuit changes in emotional processing and inhibitory control associated with clinical improvement following transference-focused psychotherapy in borderline personality disorder. \u003cb\u003e70\u003c/b\u003e, 51\u0026ndash;61 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbass, A. A. \u003cem\u003eet al.\u003c/em\u003e Review of psychodynamic psychotherapy neuroimaging studies. \u003cb\u003e83\u003c/b\u003e, 142\u0026ndash;147 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammadi, M.-R., Amini, H., Kaviani, H. \u0026amp; Shabani, A. %J I. J. P. Structured Clinical Interview for DSM-IV (SCID): Persian translation and cultural adaptation. \u003cb\u003e2\u003c/b\u003e, 46 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirst, M. B. %J B. R. D. Structured clinical interview for DSM-IV axis I disorders. (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirst, M. B., Spitzer, R. L., Gibbon, M. \u0026amp; Williams, J. B. W. \u003cem\u003eUser\u0026rsquo;s guide for the Structured clinical interview for DSM-IV axis I disorders SCID-I: clinician version\u003c/em\u003e. (American Psychiatric Pub, 1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirst, M. B., Spitzer, R. L., Gibbon, M. \u0026amp; Williams, J. B. W. \u003cem\u003eStructured clinical interview for DSM-IV-TR axis I disorders, research version, patient edition\u003c/em\u003e. (SCID-I/P New York, NY, 2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHARIFI, V. \u003cem\u003eet al.\u003c/em\u003e RELIABILITY AND FEASIBILITY OF THE PERSIAN VERSION OF THE STRUCTURED DIAGNOSTIC INTERVIEW FOR DSM-IV (SCID). \u003cb\u003e6\u003c/b\u003e, (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaffei, C. \u003cem\u003eet al.\u003c/em\u003e Interrater reliability and internal consistency of the structured clinical interview for DSM-IV axis II personality disorders (SCID-II), version 2.0. \u003cb\u003e11\u003c/b\u003e, 279\u0026ndash;284 (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroup, W. H. O. A. W. %J A. The alcohol, smoking and substance involvement screening test (ASSIST): development, reliability and feasibility. \u003cb\u003e97\u003c/b\u003e, 1183\u0026ndash;1194 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHumeniuk, R. \u003cem\u003eet al.\u003c/em\u003e Validation of the alcohol, smoking and substance involvement screening test (ASSIST). (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfohl, B. \u003cem\u003eet al.\u003c/em\u003e Reliability and Validity of the Borderline Evaluation of Severity Over Time (Best): A Self-Rated Scale to Measure Severity and Change in Persons With Borderline Personality Disorder. Journal of Personality Disorders \u003cb\u003e23\u003c/b\u003e, 281\u0026ndash;293 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzizi, M. R. \u003cem\u003eet al.\u003c/em\u003e Validity and reliability of Persian translation of the Borderline Evaluation of Severity over Time (BEST) questionnaire. \u003cb\u003e33\u003c/b\u003e, 133 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, M. H. %J P. \u0026amp; Bulletin, S. P. Empathic concern and the muscular dystrophy telethon: Empathy as a multidimensional construct. \u003cb\u003e9\u003c/b\u003e, 223\u0026ndash;229 (1983).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, M. H. %J J. of personality \u0026amp; psychology, social. Measuring individual differences in empathy: Evidence for a multidimensional approach. \u003cb\u003e44\u003c/b\u003e, 113 (1983).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, M. H. %J J. of personality. The effects of dispositional empathy on emotional reactions and helping: A multidimensional approach. \u003cb\u003e51\u003c/b\u003e, 167\u0026ndash;184 (1983).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhorbani, N., Watson, P. J., Lotfi, S., Chen Cognition, Z. %J I. \u0026amp; Personality. Moral affects, empathy, and integrative self-knowledge in Iran. \u003cb\u003e34\u003c/b\u003e, 39\u0026ndash;56 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagby, R. M., Parker, J. D. A. \u0026amp; Taylor, G. J. %J J. of psychosomatic research. The twenty-item Toronto Alexithymia Scale\u0026mdash;I. Item selection and cross-validation of the factor structure. \u003cb\u003e38\u003c/b\u003e, 23\u0026ndash;32 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagby, M., Taylor, G. J., Ryan, D. %J P. \u0026amp; Psychosomatics. Toronto Alexithymia Scale: Relationship with personality and psychopathology measures. \u003cb\u003e45\u003c/b\u003e, 207\u0026ndash;215 (1986).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBesharat, M. A. %J I. J. of M. S. Psychometric characteristics of Persian version of the Toronto alexithymia scale-20 in clinical and non-clinical samples. \u003cb\u003e33\u003c/b\u003e, 1\u0026ndash;6 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBesharat, M. A. %J P. reports. Reliability and factorial validity of a Farsi version of the 20-item Toronto Alexithymia Scale with a sample of Iranian students. \u003cb\u003e101\u003c/b\u003e, 209\u0026ndash;220 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao-Gan, Y. \u0026amp; Yu-Feng, Z. DPARSF: a MATLAB toolbox for \u0026ldquo;pipeline\u0026rdquo; data analysis of resting-state fMRI. Frontiers in systems neuroscience \u003cb\u003e4\u003c/b\u003e, (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiang, S., Stern, J. M., Engel Jr, J., Levin, H. S. \u0026amp; Haneef, Z. Differences in graph theory functional connectivity in left and right temporal lobe epilepsy. Epilepsy research \u003cb\u003e108\u003c/b\u003e, 1770\u0026ndash;1781 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTing, C.-M., Ombao, H., Samdin, S. B. \u0026amp; Salleh, S.-H. Estimating dynamic connectivity states in fMRI using regime-switching factor models. IEEE transactions on medical imaging \u003cb\u003e37\u003c/b\u003e, 1011\u0026ndash;1023 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTzourio-Mazoyer, N. \u003cem\u003eet al.\u003c/em\u003e Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage \u003cb\u003e15\u003c/b\u003e, 273\u0026ndash;289 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan den Heuvel, M. P. \u003cem\u003eet al.\u003c/em\u003e Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations. Neuroimage \u003cb\u003e152\u003c/b\u003e, 437\u0026ndash;449 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFornito, A., Zalesky, A. \u0026amp; Bullmore, E. T. Network scaling effects in graph analytic studies of human resting-state fMRI data. Frontiers in Systems Neuroscience \u003cb\u003e4\u003c/b\u003e, 1\u0026ndash;16 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFornito, A. \u003cem\u003eet al.\u003c/em\u003e Genetic influences on cost-efficient organization of human cortical functional networks. Journal of Neuroscience \u003cb\u003e31\u003c/b\u003e, 3261\u0026ndash;3270 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOldham, S. \u003cem\u003eet al.\u003c/em\u003e Consistency and differences between centrality measures across distinct classes of networks. PloS one \u003cb\u003e14\u003c/b\u003e, e0220061 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBullmore, E. \u0026amp; Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience \u003cb\u003e10\u003c/b\u003e, 186 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmiri, S., Arbabi, M., Kazemi, K., Parvaresh-Rizi, M. \u0026amp; Mirbagheri, M. M. Characterization of brain functional connectivity in treatment-resistant depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry \u003cb\u003e111\u003c/b\u003e, 110346 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRueda, D. F., Calle, E. \u0026amp; Marzo, J. L. Robustness comparison of 15 real telecommunication networks: Structural and centrality measurements. Journal of Network and Systems Management \u003cb\u003e25\u003c/b\u003e, 269\u0026ndash;289 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmiri, S. \u003cem\u003eet al.\u003c/em\u003e Graph theory application with functional connectivity to distinguish left from right temporal lobe epilepsy. Epilepsy Research \u003cb\u003e167\u003c/b\u003e, 106449 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmiri, S. \u003cem\u003eet al.\u003c/em\u003e Brain functional connectivity in individuals with psychogenic nonepileptic seizures (PNES): An application of graph theory. Epilepsy \u0026amp; Behavior \u003cb\u003e114\u003c/b\u003e, 107565 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBullmore, E. \u0026amp; Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature reviews neuroscience \u003cb\u003e10\u003c/b\u003e, 186 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNichols, T. E. \u0026amp; Holmes, A. P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human brain mapping \u003cb\u003e15\u003c/b\u003e, 1\u0026ndash;25 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMijalkov, M. \u003cem\u003eet al.\u003c/em\u003e BRAPH: A graph theory software for the analysis of brain connectivity. PloS one \u003cb\u003e12\u003c/b\u003e, e0178798 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, X., Hu, L., Zeng, J., Tan, Y. \u0026amp; Cheng, B. %J S. reports. Default mode network and frontolimbic gray matter abnormalities in patients with borderline personality disorder: a voxel-based meta-analysis. \u003cb\u003e6\u003c/b\u003e, 1\u0026ndash;10 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisintin, E. \u003cem\u003eet al.\u003c/em\u003e Mapping the brain correlates of borderline personality disorder: a functional neuroimaging meta-analysis of resting state studies. \u003cb\u003e204\u003c/b\u003e, 262\u0026ndash;269 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaczkowski, B. M. \u003cem\u003eet al.\u003c/em\u003e Deficient amygdala\u0026ndash;prefrontal intrinsic connectivity after effortful emotion regulation in borderline personality disorder. \u003cb\u003e267\u003c/b\u003e, 551\u0026ndash;565 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilbersweig, D. \u003cem\u003eet al.\u003c/em\u003e Failure of frontolimbic inhibitory function in the context of negative emotion in borderline personality disorder. \u003cb\u003e164\u003c/b\u003e, 1832\u0026ndash;1841 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraus, N. \u0026amp; Chandrasekaran, B. %J N. reviews neuroscience. Music training for the development of auditory skills. \u003cb\u003e11\u003c/b\u003e, 599\u0026ndash;605 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuocco, A. C., Amirthavasagam, S., Choi-Kain, L. W. \u0026amp; McMain, S. F. %J B. psychiatry. Neural correlates of negative emotionality in borderline personality disorder: an activation-likelihood-estimation meta-analysis. \u003cb\u003e73\u003c/b\u003e, 153\u0026ndash;160 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmoski, M. J. \u003cem\u003eet al.\u003c/em\u003e Functional imaging of emotion reactivity in opiate-dependent borderline personality disorder. Personality Disorders: Theory, Research, and Treatment \u003cb\u003e2\u003c/b\u003e, 230\u0026ndash;241 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulze, L., Schmahl, C. \u0026amp; Niedtfeld, I. %J B. psychiatry. Neural correlates of disturbed emotion processing in borderline personality disorder: a multimodal meta-analysis. \u003cb\u003e79\u003c/b\u003e, 97\u0026ndash;106 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrews-Hanna, J. R., Smallwood, J. \u0026amp; Spreng, R. N. %J A. of the N. Y. A. of S. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. \u003cb\u003e1316\u003c/b\u003e, 29 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraus, A. \u003cem\u003eet al.\u003c/em\u003e Script-driven imagery of self‐injurious behavior in patients with borderline personality disorder: A pilot FMRI study. \u003cb\u003e121\u003c/b\u003e, 41\u0026ndash;51 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrews-Hanna, J. R., Saxe, R. \u0026amp; Yarkoni, T. %J N. Contributions of episodic retrieval and mentalizing to autobiographical thought: evidence from functional neuroimaging, resting-state connectivity, and fMRI meta-analyses. \u003cb\u003e91\u003c/b\u003e, 324\u0026ndash;335 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBush, G., Luu, P. \u0026amp; Posner, M. I. Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences \u003cb\u003e4\u003c/b\u003e, 215\u0026ndash;222 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeissman, D. H., Roberts, K. C., Visscher, K. M. \u0026amp; Woldorff, M. G. %J N. neuroscience. The neural bases of momentary lapses in attention. \u003cb\u003e9\u003c/b\u003e, 971\u0026ndash;978 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNee, D. E., Wager, T. D. \u0026amp; Jonides, J. Interference resolution: Insights from a meta-analysis of neuroimaging tasks. Cognitive, Affective, \u0026amp; Behavioral Neuroscience \u003cb\u003e7\u003c/b\u003e, 1\u0026ndash;17 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWager, T. D. \u0026amp; Smith, E. E. Neuroimaging studies of working memory. Cognitive, Affective, \u0026amp; Behavioral Neuroscience \u003cb\u003e3\u003c/b\u003e, 255\u0026ndash;274 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDosenbach, N. U. F. \u003cem\u003eet al.\u003c/em\u003e A Core System for the Implementation of Task Sets. Neuron \u003cb\u003e50\u003c/b\u003e, 799\u0026ndash;812 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeeley, W. W. \u003cem\u003eet al.\u003c/em\u003e Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control. \u003cb\u003e27\u003c/b\u003e, 2349\u0026ndash;2356 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen, S. E. \u0026amp; Posner, M. I. %J A. review of neuroscience. The attention system of the human brain: 20 years after. \u003cb\u003e35\u003c/b\u003e, 73\u0026ndash;89 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiendam, T. A. \u003cem\u003eet al.\u003c/em\u003e Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cognitive, Affective, \u0026amp; Behavioral Neuroscience \u003cb\u003e12\u003c/b\u003e, 241\u0026ndash;268 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenon, V. \u0026amp; Uddin, L. Q. Saliency, switching, attention and control: a network model of insula function. Brain Structure and Function \u003cb\u003e214\u003c/b\u003e, 655\u0026ndash;667 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenon, V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences \u003cb\u003e15\u003c/b\u003e, 483\u0026ndash;506 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFichtenholtz, H. M. \u003cem\u003eet al.\u003c/em\u003e Emotion\u0026ndash;attention network interactions during a visual oddball task. \u003cb\u003e20\u003c/b\u003e, 67\u0026ndash;80 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlsson, K. \u003cem\u003eet al.\u003c/em\u003e Fear and the amygdala: manipulation of awareness generates differential cerebral responses to phobic and fear-relevant (but nonfeared) stimuli. \u003cb\u003e4\u003c/b\u003e, 340 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLane, R. D., Fink, G. R., Chau, P. M.-L. \u0026amp; Dolan, R. J. %J N. Neural activation during selective attention to subjective emotional responses. \u003cb\u003e8\u003c/b\u003e, 3969\u0026ndash;3972 (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor, S. F., Phan, K. L., Decker, L. R. \u0026amp; Liberzon, I. %J N. Subjective rating of emotionally salient stimuli modulates neural activity. \u003cb\u003e18\u003c/b\u003e, 650\u0026ndash;659 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHutcherson, C. A. \u003cem\u003eet al.\u003c/em\u003e Attention and emotion: does rating emotion alter neural responses to amusing and sad films? \u003cb\u003e27\u003c/b\u003e, 656\u0026ndash;668 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, D.-Y. \u0026amp; Lee, J.-H. Are posterior default-mode networks more robust than anterior default-mode networks? Evidence from resting-state fMRI data analysis. Neuroscience Letters \u003cb\u003e498\u003c/b\u003e, 57\u0026ndash;62 (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Default mode network, connectivity, resting-state fMRI, borderline personality disorder","lastPublishedDoi":"10.21203/rs.3.rs-1609095/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1609095/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBorderline personality disorder (BPD) is characterized by impairments in impulse control, social functioning along with a deficit in emotional awareness and empathy. In this study, nine BPD patiens filled out the demography, Interpersonal Reactive Index(IRI), Toronto Alexithymia Scale 20 (TAS 20), The Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST), and the Borderline Evaluation Severity over Time (BEST) questionnaire. Default mode network (DMN) connectivity was measured using resting-state fMRI before psychodynamic psychotherapy and then every four months for a year after initiating psychotherapy. BPD patients present with aberrant DMN connectivity compared to healthy controls. Over a year of psychotherapy, the BPD patients showed both neuroimaging changes (decreasing nodal degree connection in the dorsal anterior cingulate cortex and increasing in other cingulate cortex regions) and behavioral improvement in their symptoms and substance use. There was also a significant positive association between the decreased nodal degree in regions of the cingulate gyrus and a decrease in the score of the TAS-20 indicating difficulty in identifying feelings after psychotherapy.\u0026nbsp;In BPD, there is altered connectivity within the DMN and disruption in self-processing and emotion regulation. Psychotherapy may modify the DMN connectivity and that modification is associated with positive changes in BPD behavioral symptoms.\u003c/p\u003e","manuscriptTitle":"Default mode network connectivity as a possible biomarker for emotional self- awareness improvement in borderline personality disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-18 16:15:51","doi":"10.21203/rs.3.rs-1609095/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4741d679-8b3f-4d04-86d4-40261b21256f","owner":[],"postedDate":"May 18th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-24T06:29:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-18 16:15:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1609095","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1609095","identity":"rs-1609095","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}