Shalaila S. Haas , Gaelle E. Doucet , Mathilde Antoniades , Amirhossein Modabbernia , Cheryl M. Corcoran , René S. Kahn , Joseph Kambeitz , Lana Kambeitz-Ilankovic , Stefan Borgwardt , Paolo Brambilla , Rachel Upthegrove , Stephen J. Wood , Raimo K.R. Salokangas , Jarmo Hietala , Eva Meisenzahl , Nikolaos Koutsouleris , Sophia Frangou
{"title":"在社会功能的神经解剖学相关性中,精神病风险和非临床样本之间不连续性的证据","authors":"Shalaila S. Haas , Gaelle E. Doucet , Mathilde Antoniades , Amirhossein Modabbernia , Cheryl M. Corcoran , René S. Kahn , Joseph Kambeitz , Lana Kambeitz-Ilankovic , Stefan Borgwardt , Paolo Brambilla , Rachel Upthegrove , Stephen J. Wood , Raimo K.R. Salokangas , Jarmo Hietala , Eva Meisenzahl , Nikolaos Koutsouleris , Sophia Frangou","doi":"10.1016/j.scog.2022.100252","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>Social dysfunction is a major feature of clinical-high-risk states for psychosis (CHR-P). Prior research has identified a neuroanatomical pattern associated with impaired social function outcome in CHR-P. The aim of the current study was to test whether social dysfunction in CHR-P is neurobiologically distinct or in a continuum with the lower end of the normal distribution of individual differences in social functioning.</p></div><div><h3>Methods</h3><p>We used a machine learning classifier to test for the presence of a previously validated brain structural pattern associated with impaired social outcome in CHR-P (CHR-outcome-neurosignature) in the neuroimaging profiles of individuals from two non-clinical samples (total n = 1763) and examined its association with social function, psychopathology and cognition.</p></div><div><h3>Results</h3><p>Although the CHR-outcome-neurosignature could be detected in a subset of the non-clinical samples, it was not associated was adverse social outcomes or higher psychopathology levels. However, participants whose neuroanatomical profiles were highly aligned with the CHR-outcome-neurosignature manifested subtle disadvantage in fluid (P<sub>FDR</sub> = 0.004) and crystallized intelligence (P<sub>FDR</sub> = 0.01), cognitive flexibility (P<sub>FDR</sub> = 0.02), inhibitory control (P<sub>FDR</sub> = 0.01), working memory (P<sub>FDR</sub> = 0.0005), and processing speed (P<sub>FDR</sub> = 0.04).</p></div><div><h3>Conclusions</h3><p>We provide evidence of divergence in brain structural underpinnings of social dysfunction derived from a psychosis-risk enriched population when applied to non-clinical samples. This approach appears promising in identifying brain mechanisms bound to psychosis through comparisons of patient populations to non-clinical samples with the same neuroanatomical profiles.</p></div>","PeriodicalId":38119,"journal":{"name":"Schizophrenia Research-Cognition","volume":"29 ","pages":"Article 100252"},"PeriodicalIF":2.3000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/04/41/main.PMC8980307.pdf","citationCount":"0","resultStr":"{\"title\":\"Evidence of discontinuity between psychosis-risk and non-clinical samples in the neuroanatomical correlates of social function\",\"authors\":\"Shalaila S. Haas , Gaelle E. Doucet , Mathilde Antoniades , Amirhossein Modabbernia , Cheryl M. Corcoran , René S. Kahn , Joseph Kambeitz , Lana Kambeitz-Ilankovic , Stefan Borgwardt , Paolo Brambilla , Rachel Upthegrove , Stephen J. Wood , Raimo K.R. Salokangas , Jarmo Hietala , Eva Meisenzahl , Nikolaos Koutsouleris , Sophia Frangou\",\"doi\":\"10.1016/j.scog.2022.100252\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>Social dysfunction is a major feature of clinical-high-risk states for psychosis (CHR-P). Prior research has identified a neuroanatomical pattern associated with impaired social function outcome in CHR-P. The aim of the current study was to test whether social dysfunction in CHR-P is neurobiologically distinct or in a continuum with the lower end of the normal distribution of individual differences in social functioning.</p></div><div><h3>Methods</h3><p>We used a machine learning classifier to test for the presence of a previously validated brain structural pattern associated with impaired social outcome in CHR-P (CHR-outcome-neurosignature) in the neuroimaging profiles of individuals from two non-clinical samples (total n = 1763) and examined its association with social function, psychopathology and cognition.</p></div><div><h3>Results</h3><p>Although the CHR-outcome-neurosignature could be detected in a subset of the non-clinical samples, it was not associated was adverse social outcomes or higher psychopathology levels. However, participants whose neuroanatomical profiles were highly aligned with the CHR-outcome-neurosignature manifested subtle disadvantage in fluid (P<sub>FDR</sub> = 0.004) and crystallized intelligence (P<sub>FDR</sub> = 0.01), cognitive flexibility (P<sub>FDR</sub> = 0.02), inhibitory control (P<sub>FDR</sub> = 0.01), working memory (P<sub>FDR</sub> = 0.0005), and processing speed (P<sub>FDR</sub> = 0.04).</p></div><div><h3>Conclusions</h3><p>We provide evidence of divergence in brain structural underpinnings of social dysfunction derived from a psychosis-risk enriched population when applied to non-clinical samples. This approach appears promising in identifying brain mechanisms bound to psychosis through comparisons of patient populations to non-clinical samples with the same neuroanatomical profiles.</p></div>\",\"PeriodicalId\":38119,\"journal\":{\"name\":\"Schizophrenia Research-Cognition\",\"volume\":\"29 \",\"pages\":\"Article 100252\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/04/41/main.PMC8980307.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Schizophrenia Research-Cognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2215001322000178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Schizophrenia Research-Cognition","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2215001322000178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Evidence of discontinuity between psychosis-risk and non-clinical samples in the neuroanatomical correlates of social function
Objective
Social dysfunction is a major feature of clinical-high-risk states for psychosis (CHR-P). Prior research has identified a neuroanatomical pattern associated with impaired social function outcome in CHR-P. The aim of the current study was to test whether social dysfunction in CHR-P is neurobiologically distinct or in a continuum with the lower end of the normal distribution of individual differences in social functioning.
Methods
We used a machine learning classifier to test for the presence of a previously validated brain structural pattern associated with impaired social outcome in CHR-P (CHR-outcome-neurosignature) in the neuroimaging profiles of individuals from two non-clinical samples (total n = 1763) and examined its association with social function, psychopathology and cognition.
Results
Although the CHR-outcome-neurosignature could be detected in a subset of the non-clinical samples, it was not associated was adverse social outcomes or higher psychopathology levels. However, participants whose neuroanatomical profiles were highly aligned with the CHR-outcome-neurosignature manifested subtle disadvantage in fluid (PFDR = 0.004) and crystallized intelligence (PFDR = 0.01), cognitive flexibility (PFDR = 0.02), inhibitory control (PFDR = 0.01), working memory (PFDR = 0.0005), and processing speed (PFDR = 0.04).
Conclusions
We provide evidence of divergence in brain structural underpinnings of social dysfunction derived from a psychosis-risk enriched population when applied to non-clinical samples. This approach appears promising in identifying brain mechanisms bound to psychosis through comparisons of patient populations to non-clinical samples with the same neuroanatomical profiles.