Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes

Abstract

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Disentangling heterogeneity of psychosis expression in the general population: sex-specific moderation effects of environmental risk factors on symptom networks

“…This finding adds to previous research suggesting particularly detrimental effects of sexual and physical abuse on mental health of girls and women (Adams, Mrug, & Knight, 2018;Thompson, Kingree, & Desai, 2004). One reason for the distinct role of adversities may lie in the sex-specific effects they have on the nervous system, against the backdrop of sex differences in maturation, structure and functioning thereof (DeSantis et al, 2011;Dow-Edwards, 2020;Hodes & Epperson, 2019;Popovic et al, 2020). Moreover, characteristics of some risk factors have been shown to differ by sex: men are more likely to engage in more escalating and chronic patterns of cannabis use than women, for example (Hawes, Trucco, Duperrouzel, Coxe, & Gonzalez, 2019;Wagner & Anthony, 2007).…”

Long-term Neural Embedding of Childhood Adversity in a Population-Representative Birth Cohort Followed for 5 Decades

“…Previous studies, including the largest study of childhood adversity to date ( 7 ), have demonstrated associations with smaller gray matter volume across a number of cortical regions ( 16 , 17 , 25 , 26 , 27 ). Our findings extend these prior associations by documenting that they encompass both surface area and cortical thickness.…”

“…Identifying mechanisms through which adversity becomes biologically embedded and thereby disrupts development has been a vigorous focus of neuropsychiatric research ( 8 , 9 , 10 , 11 , 12 , 13 ). To date, research has revealed that childhood adversity is associated with alterations in the structural integrity of affective, motivational, mnemonic, and executive control networks ( 7 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ). However, the existing evidence for such biological embedding of adversity is limited in several ways.…”

Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern

“…Diese neuen Trennlinien werden basierend auf maschinellen Algorithmen unter anderem anhand klinischer [39,40], kognitiver [41,42], genetischer [43][44][45], hirnfunktioneller [46][47][48] oder hirnstruktureller Muster [49,50] entworfen. Des Weiteren wurden bereits auch Kindheitstraumata [51], Psychopathie [52], Suizidalität [53] oder Empathie [54] mithilfe nicht-supervidierter Algorithmen in verschiedene neurobiologische oder genetische Merkmalsräume dekonstruiert.…”