Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis
IntroductionBorderline personality disorder (BPD) is one of the most frequently diagnosed disorders in psychiatric settings. Beyond the categorical diagnosis, borderline personality traits (BPT) are common in the general population and vary along a continuum from mild to severe. While prior research...
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Frontiers Media S.A.
2025-07-01
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| author | Alessandro Grecucci Miriam Langerbeck Richard Bakiaj Parisa Ahmadi Ghomroudi Davide Rivolta Xiaoping Yi Xiaoping Yi Xiaoping Yi Irene Messina Irene Messina |
| author_facet | Alessandro Grecucci Miriam Langerbeck Richard Bakiaj Parisa Ahmadi Ghomroudi Davide Rivolta Xiaoping Yi Xiaoping Yi Xiaoping Yi Irene Messina Irene Messina |
| author_sort | Alessandro Grecucci |
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| description | IntroductionBorderline personality disorder (BPD) is one of the most frequently diagnosed disorders in psychiatric settings. Beyond the categorical diagnosis, borderline personality traits (BPT) are common in the general population and vary along a continuum from mild to severe. While prior research has reported functional connectivity alterations in the default mode network (DMN), the salience network (SN), and the central-executive network (CEN) in patients with BPD, the impairment of these networks in subclinical BPT remain underexplored. To fill this gap, this study aims to investigate dynamic functional connectivity alterations associated with BPT in a subclinical population. We expect to find abnormal connectivity inside the DMN, the SN and in regions ascribed to mentalization processes associated with BPT. We also expect these networks to be associated with psychological symptoms experienced by borderline patients such as impulsivity and anger issues, as well as lack of self-control and neuroticism among others.MethodAn unsupervised machine learning method known as Group-ICA, was applied to resting state fMRI images of 200 individuals to predict BPT from the temporal variability of independent macro networks.ResultsResults indicated abnormal dynamic functional connectivity inside the SN including areas implicated in emotional reactivity and sensitivity, and in a network that partially overlaps with the DMN, including regions involved in social cognition and mind reading. Specifically, the higher the BPT, the higher the temporal variability inside the SN, and the lower the temporal variability in a network that includes DMN and mentalization regions. Notably, the BOLD variability of the SN correlated with neuroticism, anger problems, lack of self- control, and distorted inner dialogue, all symptoms displayed by individuals with borderline personality.DiscussionThese findings indicate that abnormalities in resting state networks are visible in subclinical populations with varying degrees of borderline traits, with impaired DMN and SN. These insights may pave the way for designing interventions to prevent the development of the full disorder. |
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| institution | Kabale University |
| issn | 1662-5161 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Frontiers in Human Neuroscience |
| spelling | doaj-art-91e970bc4b03457ab249a80301ea620f2025-08-20T03:28:24ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612025-07-011910.3389/fnhum.2025.15894401589440Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysisAlessandro Grecucci0Miriam Langerbeck1Richard Bakiaj2Parisa Ahmadi Ghomroudi3Davide Rivolta4Xiaoping Yi5Xiaoping Yi6Xiaoping Yi7Irene Messina8Irene Messina9Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Trento, ItalyFaculty of Psychology and Neuroscience (FPN), Maastricht University, Maastricht, NetherlandsDepartment of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Trento, ItalyDepartment of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Trento, ItalyDepartment of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, ItalyDepartment of Radiology, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, ChinaClinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, ChinaSchool of Medicine, Chongqing University, Chongqing, ChinaDepartment of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Trento, ItalyDepartment of Human and Social Sciences, Mercatorum University, Rome, ItalyIntroductionBorderline personality disorder (BPD) is one of the most frequently diagnosed disorders in psychiatric settings. Beyond the categorical diagnosis, borderline personality traits (BPT) are common in the general population and vary along a continuum from mild to severe. While prior research has reported functional connectivity alterations in the default mode network (DMN), the salience network (SN), and the central-executive network (CEN) in patients with BPD, the impairment of these networks in subclinical BPT remain underexplored. To fill this gap, this study aims to investigate dynamic functional connectivity alterations associated with BPT in a subclinical population. We expect to find abnormal connectivity inside the DMN, the SN and in regions ascribed to mentalization processes associated with BPT. We also expect these networks to be associated with psychological symptoms experienced by borderline patients such as impulsivity and anger issues, as well as lack of self-control and neuroticism among others.MethodAn unsupervised machine learning method known as Group-ICA, was applied to resting state fMRI images of 200 individuals to predict BPT from the temporal variability of independent macro networks.ResultsResults indicated abnormal dynamic functional connectivity inside the SN including areas implicated in emotional reactivity and sensitivity, and in a network that partially overlaps with the DMN, including regions involved in social cognition and mind reading. Specifically, the higher the BPT, the higher the temporal variability inside the SN, and the lower the temporal variability in a network that includes DMN and mentalization regions. Notably, the BOLD variability of the SN correlated with neuroticism, anger problems, lack of self- control, and distorted inner dialogue, all symptoms displayed by individuals with borderline personality.DiscussionThese findings indicate that abnormalities in resting state networks are visible in subclinical populations with varying degrees of borderline traits, with impaired DMN and SN. These insights may pave the way for designing interventions to prevent the development of the full disorder.https://www.frontiersin.org/articles/10.3389/fnhum.2025.1589440/fullborderline personality disorderpersonality traitsunsupervised machine learningdefault mode networksalience network |
| spellingShingle | Alessandro Grecucci Miriam Langerbeck Richard Bakiaj Parisa Ahmadi Ghomroudi Davide Rivolta Xiaoping Yi Xiaoping Yi Xiaoping Yi Irene Messina Irene Messina Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis Frontiers in Human Neuroscience borderline personality disorder personality traits unsupervised machine learning default mode network salience network |
| title | Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis |
| title_full | Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis |
| title_fullStr | Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis |
| title_full_unstemmed | Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis |
| title_short | Salience and default networks predict borderline personality traits and affective symptoms: a dynamic functional connectivity analysis |
| title_sort | salience and default networks predict borderline personality traits and affective symptoms a dynamic functional connectivity analysis |
| topic | borderline personality disorder personality traits unsupervised machine learning default mode network salience network |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1589440/full |
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