Biology subjects

Rawls, E.

Publications and source records attributed to Rawls, E..

8 recordsLinked to original sources

bioRxiv2024

Causal Discovery Analysis Reveals Insights into Psychosis Proneness, Brain Function, and Environmental Factors among Young Individuals

BackgroundExperiencing symptoms of psychosis, such as delusions and hallucinations, are observed in general, nonclinical populations. These experiences are sometimes described as psychosis proneness (PP) and potentially part of the psychosis continuum. The directional relationships among various factors contributing to psychosis proneness and its interactions, encompassing both environmental and neural mechanisms, lack comprehensive description. We aimed to identify targets to prevent psychosis proneness and its interactions by characterizing pathways using causal discovery analysis (CDA). MethodsParticipants were 194 healthy adolescent and young adult twin and sibling pairs aged between 14-24 years from Turkiye. They completed comprehensive assessments evaluating sociodemographic status, environmental risk, general intelligence, self-schema, PP, and working memory (WM) performance during fMRI (37 variables). CDA was applied, a novel machine learning algorithm, to understand the causal relationships of PP. ResultsThe analysis identified negative self-schema as having the largest causal effect among all assessments in PP [Effect size (ES)= 0.55]. Secondly, social cohesion and trust (SC&T) had a protective causal effect on PP [ES= -0.18]. Lastly, PP was identified as a direct cause of greater activation in DLPFC (BA9a-BA46v) during manipulation in the WM (ES= 0.14). ConclusionsCDA provides directionality of the 37 variables which were not presented earlier. The findings highlight the significance of negative self-schema and SC&T in the general population with PP, emphasizing the potential for preventive interventions targeting these factors. These findings also suggest a role for DLPFC as a potential target in this regard. To our knowledge, this is the first study using data-driven analysis to model causal mechanisms in PP in the general population.

neuroscience↗

bioRxiv2023

NeuroFreq: a MATLAB Toolbox for Time-Frequency Analysis of M/EEG Data

Time-frequency (TF) analysis of M/EEG data enables rich understanding of cortical dynamics underlying cognition, health, and disease. There are many algorithms for time-frequency decomposition of M/EEG neural data, but they are implemented in an inconsistent manner and most existing toolboxes either 1) contain only one or a few transforms, or 2) are not adapted to analyze multichannel, multitrial M/EEG data. This makes entry into time-frequency daunting for new practitioners and limits the ability of the community to flexibly compare the performance of multiple TF methods on M/EEG data. This paper introduces the NeuroFreq toolbox for MATLAB, which includes multiple TF transformation algorithms that are implemented in a consistent fashion and produce consistent output. The toolbox includes TF decomposition algorithms of both linear and quadratic classes, utilities for resampling, averaging, and baseline correction of TF representations, and tools for visualizing and interacting with single-trial or averaged TF representations over multiple channels. This paper introduces these utilities, and applies them to synthetic and EEG data to demonstrate the NeuroFreq toolbox.

neuroscience↗

bioRxiv2023

Neural underpinnings of comorbid posttraumatic stress and excessive alcohol use: Opposing effects on loss-related mediofrontal theta in combat veterans

ObjectiveOver half of US military veterans with posttraumatic stress disorder (PTSD) use alcohol heavily, potentially to cope with their symptoms. This study investigated the neural underpinnings of PTSD symptoms and heavy drinking in veterans. We focused on brain responses to salient outcomes within predictive coding theory. This framework suggests the brain generates prediction errors (PEs) when outcomes deviate from expectations. Alcohol use might provide negative reinforcement by reducing the salience of negatively-valenced PEs and dampening experiences like loss. MethodsWe analyzed electroencephalography (EEG) responses to unpredictable gain/loss feedback in veterans of Operations Enduring and Iraqi Freedom. We used time-frequency principal components analysis of event-related potentials to isolate neural responses indicative of PEs, identifying mediofrontal theta linked to losses (feedback-related negativity, FRN) and central delta associated with gains (reward positivity, RewP). ResultsIntrusive reexperiencing symptoms of PTSD were associated with intensified mediofrontal theta signaling during losses, suggesting heightened negative PE sensitivity. Conversely, increased hazardous alcohol use was associated with reduced theta responses, implying a dampening of these negative PEs. The separate delta-RewP component showed associations with alcohol use but not PTSD symptoms. ConclusionsFindings suggest a common neural component of PTSD and hazardous alcohol use involving altered PE processing. We suggest that reexperiencing enhances the intensity of salient negative PEs, while chronic alcohol use may reduce their intensity, thereby providing negative reinforcement by muting emotional disruption from reexperienced trauma. Modifying the mediofrontal theta response could address the intertwined nature of PTSD symptoms and alcohol use, providing new avenues for treatment.

neuroscience↗

bioRxiv2022

Somatomotor Beta Bursts Mediate the Negative Impact of PTSD Severity on Conflict Monitoring

Cognitive control deficits are associated with posttraumatic stress disorder (PTSD) and may explain how reminders of past traumatic events intrude upon daily experiences of people who have experienced trauma. Lateralized somatomotor beta-band desynchronization, an electrophysiological signature of controlled movement, indexes the downstream output of cognitive control processes. Recent evidence suggests that somatomotor beta activity does not manifest as rhythmic oscillations, but instead as discrete and stochastic burst-like events. Here, we quantified the rates of lateralized somatomotor beta bursts (beta burst rates; BBR) evoked during a flanker cognitive control paradigm among United States military veterans from Operations Iraqi and Enduring Freedom (OEF/OIF) who show varying degrees of PTSD. We found BBR reflected both response direction and conflict monitoring during processing of stimuli that evoked response conflict. Impaired behavioral performance and increased peri-response BBR were related to greater posttraumatic stress symptomatology (PTSS). Critically, increased BBR mediated the link between PTSS and decreased conflict monitoring accuracy. Results suggest that poor cognitive control in PTSS reflects a failure to adaptively disinhibit target motor representations, rather than a failure to inhibit distractor representations. Thus, BBR reveal limited representation of target stimuli as a primary contributor to impaired cognitive control in PTSD. Because BBR were robustly associated with behavioral performance and exhibited high statistical reliability the index may carry utility for appraising individual differences in cognitive control in other brain disorders.

neuroscience↗

bioRxiv2021

Executive and Attentional Resting-State Hubs of the Human Cortical Connectome Revealed by a Causal Discovery Method for Data-Driven Effective Connectivity Analysis

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n=442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

neuroscience↗

bioRxiv2021

Explainable Machine Learning Analysis Reveals Gender Differences in the Phenotypic and Neurobiological Markers of Cannabis Use Disorder

BackgroundCannabis Use Disorder (CUD) has been linked to environmental, personality, mental health, neurocognitive and neurobiological risk factors. While many studies have revealed gender differences in CUD, the relative importance of these complex factors by gender has not been described. MethodsWe conducted a data-driven examination of gender differences in CUD in a community sample of young adults (Human Connectome Project [HCP]; n = 1204, 54% female). We employed state-of-the-art machine learning methods [gradient tree boosting, XGBoost] in combination with novel factor ranking tools [SHapleys Additive exPlanations (SHAP)] as an explainable machine learning approach in the multimodal data collected by the HCP (phenotypic and brain data). ResultsWe were able to successfully classify both cannabis dependence and cannabis use levels. Previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification. Predominantly-male risk factors included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, predominantly-female risk factors included environmental (low education level, low instrumental support) factors. ConclusionsOur data-driven analysis of gender differences in the multimodal risk factors underlying cannabis dependence and use levels demonstrate that environmental factors contribute more strongly to CUD in women, whereas individual factors such as personality, mental health and neurocognitive factors have a larger importance in men. This warrants further investigations, and suggests the importance of understanding how these differences relate to the development of effective treatment approaches.

neuroscience↗

bioRxiv2021

Negative Urgency as a Risk Factor for Hazardous Alcohol Use: Dual Influences of Cognitive Control and Reinforcement Processing

Negative Urgency (NU) is a prominent risk factor for hazardous alcohol use. While research has helped elucidate how NU relates to neurobiological functioning with respect to alcohol use, no known work has contextualized such functioning within existing neurobiological theories in addiction. Therefore, we elucidated mechanisms contributing to the NU-hazardous alcohol use relationship by combining NU theories with neurobiological dual models of addiction, which posit addiction is related to cognitive control and reinforcement processing. Fifty-five undergraduates self-reported NU and hazardous alcohol use. We recorded EEG while participants performed a reinforced flanker task. We measured cognitive control using N2 activation time-locked to the incongruent flanker stimulus, and we measured reinforcement processing using the feedback-related negativity (FRN) time-locked to better-than-expected negative reinforcement feedback. We modeled hazardous drinking using hierarchical regression, with NU, N2, and FRN plus their interactions as predictors. The regression model significantly predicted hazardous alcohol use, and the three-way interaction (NUxN2xFRN) significantly improved model fit. In the context of inefficient processing (i.e., larger N2s and FRNs), NU demonstrated a strong relationship with hazardous alcohol use. In the context of efficient processing (i.e., smaller N2s and FRNs), NU was unrelated to hazardous alcohol use. This analysis provides preliminary evidence that brain mechanisms of cognitive control and reinforcement processing influence the relationship between NU and hazardous alcohol use, and confirms a specific influence of negative reinforcement processing. Future clinical research could leverage these neurobiological moderators for substance misuse treatment.

neuroscience↗

bioRxiv2020

An Integrated Multimodal Model of Alcohol Use Disorder Generated by Data-Driven Causal Discovery Analysis

ObjectiveAlcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex multifactorial mechanisms driving AUD. MethodsWe used Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). Our outcome variable was number of AUD symptoms. We applied exploratory factor analysis (EFA) to parse phenotypic measures into underlying constructs, and assessed functional connectivity within 12 resting-state brain networks as an indicator of brain function. We then employed data-driven CDA to generate an integrated model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity. ResultsEFA extracted 18 factors representing the wide HCP phenotypic space (100 measures). CDA produced an integrated multimodal model, highlighting a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain function to cognition (fluid/crystalized cognition, language & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Every edge in the model was present at p < .001, and the SEM model overall provided a good fit (RMSEA = .06, Tucker-Lewis Index = .91). ConclusionsOur data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that traditional addiction models need to be expanded to highlight the importance of social factors, amongst others. Results further demonstrated that it is possible to extract a limited set of causal factors of AUD, which can inform future research aimed at tracking factors that dynamically predict alcohol use trajectories. Lastly, the presented model identified potential treatment targets for AUD, including neuromodulation of the frontoparietal network, cognitive/affective interventions, and social interventions.

neuroscience↗