Predicting the knowledge–recklessness distinction in the human brain

Vilares, Iris; Wesley, Michael J.; Ahn, Woo-Young; Bonnie, Richard J.; Hoffman, Morris B.; Jones, Owen D.; Morse, Stephen J.; Yaffe, Gideon; Lohrenz, Terry; Montague, Read

doi:10.1073/pnas.1619385114

Cited by 44 publications

(20 citation statements)

References 32 publications

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“…Recent advances in neuroimaging pattern classification methods and prediction tools have been promising with regard to single subject prediction ( 56 ), in particular if combined with clinical and behavioral data ( 57 , 58 ). Of specific interest to the forensic use of neuroimaging, machine learning methods have been used for neuroimaging-based classification of psychopaths ( 59 ), to predict psychosis course ( 58 , 60 , 61 ), and culpability ( 62 ), as a proof of concept to show the potential of such methods. If neuroimaging methods could help to predict future violent behavior, this would impact the legal system with regard to sentencing, crime prevention and treatment ( 63 ).…”

Section: Discussionmentioning

confidence: 99%

Imaging Violence in Schizophrenia: A Systematic Review and Critical Discussion of the MRI Literature

2018

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Background: Persons with schizophrenia have a small but significant increase in risk of violence, which remains after controlling for known environmental risk factors. In vivo MRI-studies may point toward the biological underpinnings of psychotic violence, and neuroimaging has increasingly been used in forensic and legal settings despite unclear relevance.Objectives: (1) To present the first systematic review, following standardized guidelines, of MRI studies of violence with schizophrenia. (2) To critically discuss the promises and pitfalls of using this literature to understand violence in schizophrenia in clinical, forensic, and legal settings.Methods: Following the PRISMA guidelines and literature searches until January 2018, we found 21 original studies that fulfilled the inclusion criteria: (1) Studies of persons with schizophrenia, (2) a history of violence or aggressive behavior, (3) the use of one or more MRI-modalities (sMRI, DTI, fMRI).Results: The most consistent findings from the structural studies were reduced volumes of the hippocampus and the frontal lobe (in particular the orbitofrontal and anterior cingulate cortex) in schizophrenia patients with a history of violence or higher aggression scores. The functional studies mainly showed differences and aggression correlates in the frontal lobe and amygdala. However, the studies were methodologically heterogeneous, with four particular areas of concern: different definitions of violence, region of interest vs. whole-brain studies, small subject samples, and group comparisons in a heterogeneous diagnostic category (schizophrenia).Conclusion: The literature reports subtle, but inconsistent group level differences in brain structure and function associated with violence and aggression with schizophrenia, in particular in areas involved in the formation of psychosis symptoms and affective regulation. Due to methodological challenges the results should be interpreted with caution. In order to come closer to the neurobiological underpinnings of violence in schizophrenia future studies could: (1) address the neurobiological differences of premeditated and reactive violence, (2) use RDoC criteria, for example, or other symptom-based systems to categorize psychosis patients, (3) increase subject cohorts and apply new data driven methods. In this perspective, MRI-studies of violence in schizophrenia have the potential to inform clinical violence prediction and legal evaluations in the future.

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Section: Discussionmentioning

confidence: 99%

Imaging Violence in Schizophrenia: A Systematic Review and Critical Discussion of the MRI Literature

2018

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“…The p -value of the initial model fit in the out-of-sample test set was computed as the proportion of iterations in the null distribution with model performance exceeding that of the initial model fit. Application and validation of recommended best practice for the regularised regression protocol are detailed elsewhere 72,73 . The entire procedure was repeated for cortical thickness and surface area measures.…”

Section: Methodsmentioning

confidence: 99%

Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders

Pua

Ball

Adamson

et al. 2019

Sci Rep

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The neurobiology of heterogeneous neurodevelopmental disorders such as autism spectrum disorders (ASD) are still unclear. Despite extensive efforts, most findings are difficult to reproduce due to high levels of individual variance in phenotypic expression. To quantify individual differences in brain morphometry in ASD, we implemented a novel subject-level, distance-based method on subject-specific attributes. In a large multi-cohort sample, each subject with ASD (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.58) was strictly matched to a control participant (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.70). Intrapair Euclidean distance of MRI brain morphometry and symptom severity measures (Social Responsiveness Scale) were entered into a regularised machine learning pipeline for feature selection, with rigorous out-of-sample validation and permutation testing. Subject-specific structural morphometry features significantly predicted individual variation in ASD symptom severity (19 cortical thickness features, p = 0.01, n = 5000 permutations; 10 surface area features, p = 0.006, n = 5000 permutations). Findings remained robust across subjects and were replicated in validation samples. Identified cortical regions implicate key hubs of the salience and default mode networks as neuroanatomical features of social impairment in ASD. Present results highlight the importance of subject-level markers in ASD, and offer an important step forward in understanding the neurobiology of heterogeneous disorders.

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“…One thousand different divisions of training/test sets were tested to check if the performance of LASSO is robust across the divisions. Extensive details on the model fitting procedures are described in a previous study (Ahn et al, 2014) and validated in multiple studies Vilares et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Minority stress, emotion regulation, and substance misuse in sexual minorities

Rogers¹,

Dyk²,

Haines³

et al. 2017

Preprint

Self Cite

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Lesbian, gay, and bisexual (LGB) individuals report higher levels of alcohol and substance misuse than their heterosexual peers, and previous research has linked these increased rates to LGB-specific stressors, termed minority stress. However, not everyone experiencing minority stress reports increased rates of alcohol and substance misuse. Emotion regulation (ER), which plays a critical role in psychopathology generally, is postulated to modulate the link between minority stress and psychopathology. However, it remains largely unknown whether ER plays a role in linking instances of minority stress with substance and alcohol use outcomes. To address the gap, the current study assessed 305 LGB individuals’ instances of minority stress, ER, and substance and alcohol use outcomes. We assessed the role of ER in alcohol and substance misuse among LGB individuals using moderated logistic regression and exploratory machine learning analyses. Moderated binary logistic regressions revealed that emotional non-acceptance moderated the relationship between minority stress and problematic substance use, such that those with poor ER were more likely to have problematic substance use. Additionally, deficits in goal-directed behavior moderated the relationship between minority stress and alcohol and substance use, but those with more deficits in goal-directed behavior had better outcomes. Exploratory machine learning analysis revealed multivariate patterns of additional facets of ER and psychiatric symptoms that are predictive of alcohol and substance use. These results highlight the important explanatory role that ER may have between instances of minority stress and substance and alcohol use in LGB individuals.

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Predicting the knowledge–recklessness distinction in the human brain

Cited by 44 publications

References 32 publications

Imaging Violence in Schizophrenia: A Systematic Review and Critical Discussion of the MRI Literature

Imaging Violence in Schizophrenia: A Systematic Review and Critical Discussion of the MRI Literature

Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders

Minority stress, emotion regulation, and substance misuse in sexual minorities

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