Browsing by Author "Denomme, William James"

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Neuroimaging metrics of drug- and food-cue reactivity as a function of psychopathic traits, substance use, and substance dependence
(2018-08-01) Denomme, William James; Shane, Matthew S.
We conducted two fMRI studies assessing the relationship between psychopathy and drug- and food-related neural reactivity. In the first study, we assessed the relationship between psychopathic traits and neural reactivity among 47 cocaine-dependent and 58 non-dependent participants. The cocaine-dependent group exhibited a neural processing bias towards drugrelated stimuli within a corticolimbic circuit involved in decision-making, salience attribution, and motivation. Psychopathic traits both sensitized this neural processing bias and modulated the effect of substance use severity. In the second study, we separated dependent participants into psychologically- (n =25) or physiologically-dependent (n = 20) participants and observed a neural processing bias towards drug-related stimuli among physiologically-dependent participants alone. Interestingly, both psychopathic traits and substance use severity exhibited positive correlations to drug > food reactivity within psychologically-dependent participants. These results further our understanding of the comorbidity between psychopathy and addiction and help conceptualize a new comprehensive model for the development of addiction.
Neuroimaging metrics of externalizing disorders: assessing neurobiological features of substance use, withdrawal, substance use disorders, and psychopathy
(2024-05-01) Denomme, William James; Shane, Matthew
Neuroimaging research has provided several insights into the neurobiological correlates of externalizing features, notably substance use, substance use disorders, and antisociality. For instance, researchers have paired neuroimaging metrics with machine learning (ML) algorithms to classify externalizing patients from controls and externalizing patients with varying severity and prognoses. In addition, studies have used neural reactivity to drug and food rewards to separate cocaine-dependent participants from non-dependent controls, as well as cocaine-dependent with and without a history of withdrawal symptoms and varying degrees of historical cocaine use and psychopathic traits. However, variability in the classification accuracy of ML models precludes inferences of how well neuroimaging metrics can distinguish externalizing patients and controls. Moreover, variability in the classification accuracy of ML models and the lack of work using modalities outside of cue-reactivity preclude sound inferences on how well neuroimaging can distinguish subgroups of externalizing patients. This dissertation consists of three studies to address these factors. In Study 1, a meta-analysis of 49 ML models with neuroimaging predictors demonstrated that neuroimaging metrics could distinguish externalizing patients and controls with an accuracy of ~80%. Study 1 also demonstrated a classification accuracy of ~79% when distinguishing externalizing patients with severity and prognosis differences. However, it is important to note that most studies included in this meta-analysis validated their results using cross-validation, which may have inflated their classification accuracy. Next, Studies 2 and 3 demonstrated that cocaine-dependent participants were distinct from non-dependent controls in terms of gray matter concentration (GMC) or functional connectivity (FNC) in response to drug and food rewards within the orbitofrontal cortex, middle temporal gyrus, dorsolateral prefrontal cortex, middle frontal gyrus (MFG), and anterior cingulate cortex. These studies also found that GMC and FNC within several corticolimbic regions, notably the MFG, separated cocaine-dependent participants with and without a history of withdrawal and varying degrees of lifetime history of cocaine use and psychopathic traits. These results provide preliminary evidence that neuroimaging metrics could distinguish externalizing patients from control, and separate externalizing patients that are subgrouped by symptomology, severity, and prognosis. The presented work has substantial implications for developing novel assessment protocols and optimal treatment strategies.