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      Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition

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          Abstract

          Background

          Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance.

          Methods

          Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities.

          Results

          The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample ( p = 0.03).

          Conclusion

          Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40359-021-00552-3.

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          Most cited references63

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            What is a support vector machine?

            Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?
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              Global assessment of functioning. A modified scale.

              The modified Global Assessment of Functioning (GAF) scale has more detailed criteria and a more structured scoring system than the original GAF. The two scales were compared for reliability and validity. Raters who had different training levels assigned hospital admission and discharge GAF scores from patient charts. Intraclass correlation coefficients for admission GAF scores were higher for raters who used the modified GAF (0.81), compared with raters who used the original GAF (0.62). Validity studies showed a high correlation (0.80) between the two sets of scores. The modified GAF also correlated well with Zung Depression scores (-0.73). The modified GAF may be particularly useful when interrater reliability needs to be maximum and/or when persons with varying skills and employment backgrounds--and without much GAF training--must rate patients. Because of the increased structure, the modified GAF may also be more resistant to rater bias.

                Author and article information

                Contributors
                lindaaantonucci@gmail.com
                Journal
                BMC Psychol
                BMC Psychol
                BMC Psychology
                BioMed Central (London )
                2050-7283
                23 March 2021
                23 March 2021
                2021
                : 9
                : 47
                Affiliations
                [1 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Department of Education, Psychology, Communication, , University of Bari Aldo Moro, ; Via Scipione Crisanzio 42, 70122 Bari, Italy
                [2 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Department of Basic Medical Science, Neuroscience and Sense Organs, , University of Bari Aldo Moro, ; Bari, Italy
                [3 ]GRID grid.429552.d, Lieber Institute for Brain Development, , Johns Hopkins Medical Campus, ; Baltimore, MD USA
                [4 ]GRID grid.7644.1, ISNI 0000 0001 0120 3326, Psychiatry Unit, , Bari University Hospital, ; Bari, Italy
                Author information
                http://orcid.org/0000-0002-7919-7402
                Article
                552
                10.1186/s40359-021-00552-3
                7989088
                33757595
                4bdef850-96dd-4b69-907a-7fb53fc5e2e5
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 9 September 2020
                : 16 March 2021
                Funding
                Funded by: Apulia Region funding “Early identification of psychiatric risk: a longitudinal study on endophenotypes involved in the psychosis and bipolar disorder risk”
                Funded by: Structural European Funding of the Italian Minister of Education and Research (Attraction and International Mobility – AIM - action, grant agreement No 1859959)
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                parental care,parental overprotection,adult attachment style,machine learning,schizophrenia,bipolar disorder,risk for psychosis

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