Background: Multiple psychopathologies feature impaired clinical insight. Emerging evidence suggests that insight problems may similarly characterize addiction, perhaps due to aberrant functioning of self-referential brain circuitry, including the rostral anterior cingulate and ventromedial prefrontal cortices (rACC/vmPFC). We developed a new fMRI task to probe whether rACC/vmPFC abnormalities in cocaine use disorder (CUD) constitute neural correlates of readiness to change, one facet of insight.
Methods: Eighteen individuals with current CUD and 15 healthy controls responded about their own need to change their drug use and eating behavior (control condition) and the need for a named acquaintance to do the same (two additional control conditions). Measures of simulated drug-choice behavior, addiction severity, and neuropsychological function were collected outside the scanner.
Results: CUD participants perceived a greater need for behavior change than controls (as expected, given their diagnosis), but fell short of "agreeing" to a need for change; in CUD, lower perceived need correlated with higher simulated drug-choice behavior, a proxy measure of drug-seeking. During drug-related insight judgments, CUD participants had higher activation than controls in an anatomically-defined region of interest (ROI) in the medial orbitofrontal cortex, part of the rACC/vmPFC. Although not showing group differences, activation in an anatomically-defined ACC ROI correlated with insight-related task behavior (in all participants) and memory performance (in CUD).
Conclusions: As a group, individuals with current CUD appear to show mild insight problems and rACC/vmPFC abnormalities vis-à-vis readiness to change behavior. With replication and extension of these results, insight-related circuitry may emerge as a novel therapeutic target.
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http://dx.doi.org/10.1016/j.drugalcdep.2020.107930 | DOI Listing |
Schizophr Bull
January 2025
Psychology, Michigan State University, East Lansing, MI, 48824, United States.
Background And Hypothesis: Sequential saccade planning requires corollary discharge (CD) signals that provide information about the planned landing location of an eye movement. These CD signals may be altered among individuals with schizophrenia (SZ), providing a potential mechanism to explain passivity and anomalous self-experiences broadly. In healthy controls (HC), a key oculomotor CD network transmits CD signals from the thalamus to the frontal eye fields (FEF) and the intraparietal sulcus (IPS) and also remaps signals from FEF to IPS.
View Article and Find Full Text PDFHum Brain Mapp
February 2025
Department of Psychological and Brain Sciences, University of Delaware, Newark, Delaware, USA.
Converging lines of research indicate that inhibitory control is likely to be compromised in contexts that place competing demands on emotional, motivational, and cognitive systems, potentially leading to damaging impulsive behavior. The objective of this study was to identify the neural impact of three challenging contexts that typically compromise self-regulation and weaken impulse control. Participants included 66 healthy adults (M/SD = 29.
View Article and Find Full Text PDFPsychopharmacology (Berl)
January 2025
Edith Collins Centre for Translational Research in Alcohol, Drugs and Toxicology, Royal Prince Alfred Hospital, Sydney Local Health District, Sydney, NSW, Australia.
Rationale: Both topiramate and naltrexone have been shown to affect neural alcohol cue reactivity in alcohol use disorder (AUD). However, their comparative effects on alcohol cue reactivity are unknown. Moreover, while naltrexone has been found to normalize hyperactive localized network connectivity implicated in AUD, no studies have examined the effect of topiramate on intrinsic functional connectivity or compared functional connectivity between these two widely used medications.
View Article and Find Full Text PDFJ Imaging
December 2024
Technology Department, CERN, 1211 Geneva, Switzerland.
Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH).
View Article and Find Full Text PDFBrain Sci
December 2024
Electric and Electronic Engineering Department, Istanbul University-Cerrahpasa, Istanbul 34320, Turkey.
Background/objectives: This research investigates brain connectivity patterns in reaction to social and non-social stimuli within a virtual reality environment, emphasizing their impact on cognitive functions, specifically working memory.
Methods: Employing the LEiDA framework with EEG data from 47 participants, I examined dynamic brain network states elicited by social avatars compared to non-social stick cues during a VR memory task. Through the integration of LEiDA with deep learning and graph theory analyses, unique connectivity patterns associated with cue type were discerned, underscoring the substantial influence of social cues on cognitive processes.
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