Altered power spectra in antisocial males during rest as a function of cocaine dependence: A network analysis

Simard, Isabelle; Denomme, William James; Shane, Matthew S.

doi:10.1016/j.pscychresns.2020.111235

Cited by 3 publications

(2 citation statements)

References 71 publications

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“…Cha et al (2016), for instance, used a latent factor approach to delineate shared/unique neurobiological components of Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD; Cha et al, 2016), where comorbidity of symptom characteristics has long been acknowledged (Hamilton et al, 2014). Similar modelling of shared/unique variance has rarely been undertaken within the externalizing domain (but see more rudimentary approaches recently undertaken by Denomme et al, 2018;Denomme & Shane, 2020;Simard et al, 2021), but may be equally informative for distinguishing the extent to which specific neurobiological abnormalities relate to core underlying antisocial characteristics, to consequences of an antisocial/substance-abusing lifestyle, or to shared latent factors. Indeed, HiTOP currently conceptualizes externalizing as composed of two higher-order factors (disinhibited/ antagonistic externalizing), and the triarchic model of psychopathy has conceptualized psychopathy as the higher-order factor of three subdimensions (boldness, meanness, and disinhibition).…”

Section: Latent Factor Approachesmentioning

confidence: 99%

“…A small amount of work outside the machine learning literature has moved in this direction. For instance, Denomme et al, 2018 used fMRI to identify regions related to enhanced cue reactivity in cocaine-dependent individuals, and undertook subsequent regression analyses to dissect the extent to which activity within each of these regions related more closely to continuously derived metrics of psychopathic traits (i.e., PCL-R total score) or substance use severity (i.e., years of lifetime use; see also Denomme & Shane, 2020;Simard et al, 2021). And utilizing a quite distinct approach, Hyatt et al (2019) created neuroanatomical profiles (based on their five-factor traits) on 1101 participants from the Human Connectome Project, and reported that while neuroanatomical profiles of Agreeableness and Conscientiousness showed medium-to-large relationships with externalizing psychopathology, neuroanatomical profiles of Agreeableness related more closely to metrics of antisocial behavior, while neuroanatomical profiles of Conscientiousness related more closely to metrics of substance abuse.…”

Section: Collect Data On Both Addiction and Antisocialitymentioning

confidence: 99%

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Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

Shane

Denomme

2021

Personality Neuroscience

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By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

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Section: Latent Factor Approachesmentioning

confidence: 99%

Section: Collect Data On Both Addiction and Antisocialitymentioning

confidence: 99%

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

Shane

Denomme

2021

Personality Neuroscience

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View full text Add to dashboard Cite

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The neurofunctional basis of human aggression varies by levels of femininity

Liu,

Zhao,

Ding

et al. 2024

Social Neuroscience

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Psychopathology of antisocial personality disorder: from the structural, functional and biochemical perspectives

Wong

2023

Egypt J Neurol Psychiatry Neurosurg

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Background Antisocial personality disorder (ASPD) is characterized by a lack of empathy, a sense of guiltlessness and shamelessness, as well as impulsiveness. ASPD is a relatively common psychiatric condition in the general population, whereas individuals with ASPD often have substantial social impairments and a lower quality of life, especially for those who have mental comorbidities. This review gives an overview of the etiological and clinical aspects of ASPD and critically examines ASPD from the structural, functional and biochemical perspectives. Results Twin and family studies showed genetic predisposition in ASPD. Some candidate genes associated with ASPD include SLC6A4, COMT, 5-HTR2A, TPH1, DRD2, OXTR, CACNG8, COL25A1 and several serotonergic genes. Environmental factors like adverse childhood experience (ACE) and active empathy deficits in toddlerhood play a role in the etiology of ASPD, whereas low intelligence or attainment, a large family size, a convicted parent, a disrupted family, and a young mother are predictors of antisocial personality. Structural abnormalities involving the corpus callosum, amygdala, putamen, anterior cingulate cortex, as well as orbitofrontal- and dorsolateral frontal cortices have been identified in ASPD. Other observed structural changes include a decrease in grey matter volume, whole-brain volume, and white matter volume and thickness. In addition, functional abnormalities involving autonomic activity, prefrontal functions, as well as brain functional networks like sensorimotor networks, cognitive networks and cortico-striatal connectivity have been reported. Biochemical factors associated with ASPD include fatty acid amide hydrolase (FAAH) reduction in the amygdala, as well as changes in plasma levels of inflammatory biomarkers and neurotropic factors [namely, tumor necrosis factor (TNF)-α, interleukin 10 (IL-10), transforming growth factor (TGF)-β1 and brain-derived neurotrophic factor (BNDF). Increased plasma levels of testosterone, ghrelin and cortisol and decreased levels of leptin have also been implicated in ASPD. Conclusions To date, there is no Food and Drug Administration (FDA) approved drugs for ASPD. Understanding the disease from different perspectives is important, as this provides insights into the underlying mechanisms of ASPD, whereas the associated biochemical markers can be used as potential diagnostic and treatment targets for ASPD.

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Altered power spectra in antisocial males during rest as a function of cocaine dependence: A network analysis

Cited by 3 publications

References 71 publications

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

The neurofunctional basis of human aggression varies by levels of femininity

Psychopathology of antisocial personality disorder: from the structural, functional and biochemical perspectives

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