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Hardwired for suspicion. Network-level markers of paranoid personality traits revealed by multimodal machine-learning.

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Τίτλος: Hardwired for suspicion. Network-level markers of paranoid personality traits revealed by multimodal machine-learning.
Συγγραφείς: Grecucci A; Clincial and Affective Neuroscience Lab - Cli.A.N. Lab, Department of Education, Psychology and Communication Sciences, University of Bari, Italy. Electronic address: alessandro.grecucci@uniba.it., Pizzocaro L; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy., Manente L; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy., Rizzi R; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy., Taskiran E; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy., Messina I; Department of Human and Social Sciences, Faculty of Social andCommunication Sciences, Universitas Mercatorum, Rome, Italy., Bruno F; Department of Human and Social Sciences, Faculty of Social andCommunication Sciences, Universitas Mercatorum, Rome, Italy., Draghetti S; AUSL Bologna, Bologna, Italy., Stella M; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy., Yi X; Department of Nuclear Medicine and Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing 404000, PR China; Medical Imaging Center (MIC), Clinical 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 404000, PR China; School of Medicine, Chongqing University, Chongqing 400030, PR China. Electronic address: yixiaoping@cqu.edu.cn.
Πηγή: Psychiatry research. Neuroimaging [Psychiatry Res Neuroimaging] 2026 Sep; Vol. 361, pp. 112235. Date of Electronic Publication: 2026 May 02.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 101723001 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7506 (Electronic) Linking ISSN: 09254927 NLM ISO Abbreviation: Psychiatry Res Neuroimaging Subsets: MEDLINE
Imprint Name(s): Original Publication: Amsterdam : Elsevier
Ιατρικοί όροι (MeSH): Gray Matter*/diagnostic imaging , Gray Matter*/pathology , Nerve Net*/diagnostic imaging , Nerve Net*/physiopathology , Personality*/physiology , Paranoid Personality Disorder*/diagnostic imaging , Paranoid Personality Disorder*/physiopathology , Default Mode Network*/diagnostic imaging , Default Mode Network*/physiopathology , Machine Learning*, Magnetic Resonance Imaging/methods ; Humans ; Female ; Male ; Adult ; Young Adult
Περίληψη: Background: Current neuroimaging research on paranoid traits remains limited. Existing studies have largely relied on small samples, focused on categorical diagnoses or transient paranoid states, and examined either structural or functional measures in isolation. Crucially, the joint contribution of brain structure and intrinsic functional activity to paranoid personality traits (PPT) in the general population and their relationship with other psychological traits, remains unknown OBJECTIVES: The present study aimed to identify network-level neural markers of paranoid personality traits in a large sample by integrating gray matter morphology and resting-state brain activity, to test the hypothesis that networks associated with social and affective dysfunctions, predict PTT METHODS: We applied an unsupervised multimodal data-fusion approach (parallel ICA) to gray matter concentration and fractional ALFF in 197 healthy individuals. Paranoid personality traits were assessed dimensionally. In complementary analyses, we examined whether multimodal component loadings captured trait-related variance beyond demographic covariates RESULTS: Analyses identified a resting-state component encompassing the precuneus and angular gyrus, partially overlapping with the default mode network, significantly associated with paranoid personality traits. This functional component was positively correlated with a gray matter component including orbitofrontal and insular regions indicating a linked structural-functional pattern.
Conclusions: By jointly modeling gray matter and resting-state activity, this study provides the first multimodal evidence of network-level markers underlying paranoid personality traits in the general population.
(Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.)
Competing Interests: Declaration of competing interest Authors declare no competing interests.
Contributed Indexing: Keywords: Default mode network; Machine learning; Parallel ICA; Paranoid personality
Entry Date(s): Date Created: 20260509 Date Completed: 20260610 Latest Revision: 20260610
Update Code: 20260611
DOI: 10.1016/j.pscychresns.2026.112235
PMID: 42105422
Βάση Δεδομένων: MEDLINE
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  Data: Hardwired for suspicion. Network-level markers of paranoid personality traits revealed by multimodal machine-learning.
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  Data: <searchLink fieldCode="AU" term="%22Grecucci+A%22">Grecucci A</searchLink>; Clincial and Affective Neuroscience Lab - Cli.A.N. Lab, Department of Education, Psychology and Communication Sciences, University of Bari, Italy. Electronic address: alessandro.grecucci@uniba.it.<br /><searchLink fieldCode="AU" term="%22Pizzocaro+L%22">Pizzocaro L</searchLink>; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy.<br /><searchLink fieldCode="AU" term="%22Manente+L%22">Manente L</searchLink>; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy.<br /><searchLink fieldCode="AU" term="%22Rizzi+R%22">Rizzi R</searchLink>; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy.<br /><searchLink fieldCode="AU" term="%22Taskiran+E%22">Taskiran E</searchLink>; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy.<br /><searchLink fieldCode="AU" term="%22Messina+I%22">Messina I</searchLink>; Department of Human and Social Sciences, Faculty of Social andCommunication Sciences, Universitas Mercatorum, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Bruno+F%22">Bruno F</searchLink>; Department of Human and Social Sciences, Faculty of Social andCommunication Sciences, Universitas Mercatorum, Rome, Italy.<br /><searchLink fieldCode="AU" term="%22Draghetti+S%22">Draghetti S</searchLink>; AUSL Bologna, Bologna, Italy.<br /><searchLink fieldCode="AU" term="%22Stella+M%22">Stella M</searchLink>; Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Italy.<br /><searchLink fieldCode="AU" term="%22Yi+X%22">Yi X</searchLink>; Department of Nuclear Medicine and Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing 404000, PR China; Medical Imaging Center (MIC), Clinical 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 404000, PR China; School of Medicine, Chongqing University, Chongqing 400030, PR China. Electronic address: yixiaoping@cqu.edu.cn.
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  Data: <searchLink fieldCode="MM" term="%22Gray+Matter%22">Gray Matter*</searchLink>/<searchLink fieldCode="MM" term="%22Gray+Matter+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Gray+Matter%22">Gray Matter*</searchLink>/<searchLink fieldCode="MM" term="%22Gray+Matter+pathology%22">pathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Nerve+Net%22">Nerve Net*</searchLink>/<searchLink fieldCode="MM" term="%22Nerve+Net+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Nerve+Net%22">Nerve Net*</searchLink>/<searchLink fieldCode="MM" term="%22Nerve+Net+physiopathology%22">physiopathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Personality%22">Personality*</searchLink>/<searchLink fieldCode="MM" term="%22Personality+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Paranoid+Personality+Disorder%22">Paranoid Personality Disorder*</searchLink>/<searchLink fieldCode="MM" term="%22Paranoid+Personality+Disorder+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Paranoid+Personality+Disorder%22">Paranoid Personality Disorder*</searchLink>/<searchLink fieldCode="MM" term="%22Paranoid+Personality+Disorder+physiopathology%22">physiopathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Default+Mode+Network%22">Default Mode Network*</searchLink>/<searchLink fieldCode="MM" term="%22Default+Mode+Network+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Default+Mode+Network%22">Default Mode Network*</searchLink>/<searchLink fieldCode="MM" term="%22Default+Mode+Network+physiopathology%22">physiopathology</searchLink> <br /><searchLink fieldCode="MM" term="%22Machine+Learning%22">Machine Learning*</searchLink><br /><searchLink fieldCode="MH" term="%22Magnetic+Resonance+Imaging%22">Magnetic Resonance Imaging</searchLink>/<searchLink fieldCode="MH" term="%22Magnetic+Resonance+Imaging+methods%22">methods</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Adult%22">Adult</searchLink> ; <searchLink fieldCode="MH" term="%22Young+Adult%22">Young Adult</searchLink>
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  Data: Background: Current neuroimaging research on paranoid traits remains limited. Existing studies have largely relied on small samples, focused on categorical diagnoses or transient paranoid states, and examined either structural or functional measures in isolation. Crucially, the joint contribution of brain structure and intrinsic functional activity to paranoid personality traits (PPT) in the general population and their relationship with other psychological traits, remains unknown OBJECTIVES: The present study aimed to identify network-level neural markers of paranoid personality traits in a large sample by integrating gray matter morphology and resting-state brain activity, to test the hypothesis that networks associated with social and affective dysfunctions, predict PTT METHODS: We applied an unsupervised multimodal data-fusion approach (parallel ICA) to gray matter concentration and fractional ALFF in 197 healthy individuals. Paranoid personality traits were assessed dimensionally. In complementary analyses, we examined whether multimodal component loadings captured trait-related variance beyond demographic covariates RESULTS: Analyses identified a resting-state component encompassing the precuneus and angular gyrus, partially overlapping with the default mode network, significantly associated with paranoid personality traits. This functional component was positively correlated with a gray matter component including orbitofrontal and insular regions indicating a linked structural-functional pattern.<br />Conclusions: By jointly modeling gray matter and resting-state activity, this study provides the first multimodal evidence of network-level markers underlying paranoid personality traits in the general population.<br /> (Copyright © 2026 The Authors. Published by Elsevier B.V. All rights reserved.)
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  Data: Declaration of competing interest Authors declare no competing interests.
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