Charting the neural circuits disruption in inhibitory control and its subcomponents across psychiatric disorders: A neuroimaging meta-analysis

Concurrent and longitudinal neurostructural correlates of irritability in children

Irritability, or an increased proneness to frustration and anger, is common in youth; however, few studies have examined neurostructural correlates of irritability in children. The purpose of the current study was to examine concurrent and longitudinal associations between brain structure and irritability in a large sample of 9-10-year-old children. Participants included 10,647 children from the Adolescent Brain Cognitive Developmentsm Study (ABCD Study®). We related a latent irritability factor to gray matter volume, cortical thickness, and surface area in 68 cortical regions and to gray matter volume in 19 subcortical regions using structural equation modeling. Multiple comparisons were adjusted for using the false discovery rate (FDR). After controlling for age, sex, race/ethnicity, scanner model, parent's highest level of education, medication use, and total intracranial volume, irritability was associated with smaller volumes in primarily temporal and parietal regions at baseline. Longitudinal analyses showed that baseline gray matter volume did not predict irritability symptoms at the 3rd-year follow-up. No significant associations were found for cortical thickness or surface area. The current study demonstrates inverse associations between irritability and volume in regions implicated in emotional processing/social cognition, attention allocation, and movement/perception. We advance prior research by demonstrating that neurostructural differences associated with irritability are already apparent by age 9-10 years, extending this work to children and supporting theories positing socioemotional deficits as a key feature of irritability.

Aggregated Pattern Classification Method for improving neural disorder stage detection

BACKGROUND

Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach-the Aggregated Pattern Classification Method (APCM)-for precise identification of neural disorder stages.

METHOD

The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data.

RESULTS

The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm.

CONCLUSION

The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high-quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.

Neurobiology and systems biology of stress resilience

Stress resilience is the phenomenon that some people maintain their mental health despite exposure to adversity or show only temporary impairments followed by quick recovery. Resilience research attempts to unravel the factors and mechanisms that make resilience possible and to harness its insights for the development of preventative interventions in individuals at risk for acquiring stress-related dysfunctions. Biological resilience research has been lagging behind the psychological and social sciences but has seen a massive surge in recent years. At the same time, progress in this field has been hampered by methodological challenges related to finding suitable operationalizations and study designs, replicating findings, and modeling resilience in animals. We embed a review of behavioral, neuroimaging, neurobiological, and systems biological findings in adults in a critical methods discussion. We find preliminary evidence that hippocampus-based pattern separation and prefrontal-based cognitive control functions protect against the development of pathological fears in the aftermath of singular, event-type stressors [as found in fear-related disorders, including simpler forms of posttraumatic stress disorder (PTSD)] by facilitating the perception of safety. Reward system-based pursuit and savoring of positive reinforcers appear to protect against the development of more generalized dysfunctions of the anxious-depressive spectrum resulting from more severe or longer-lasting stressors (as in depression, generalized or comorbid anxiety, or severe PTSD). Links between preserved functioning of these neural systems under stress and neuroplasticity, immunoregulation, gut microbiome composition, and integrity of the gut barrier and the blood-brain barrier are beginning to emerge. On this basis, avenues for biological interventions are pointed out.

Neural underpinnings of response inhibition in substance use disorders: weak meta-analytic evidence for a widely used construct

RATIONALE

Substance use disorders (SUDs) rank among the most severely debilitating psychiatric conditions. Among others, decreased response inhibition capacities could make it more difficult for patients to abstain from drug use and maintain abstinence. However, meta-analyses on the neural basis of response inhibition in SUDs yielded conflicting results.

OBJECTIVE

In this study, we revisited the neuroimaging research field and summarized the existing fMRI literature on overt response inhibition (Go/NoGo and stop-signal paradigms) across different SUDs.

METHODS

We performed a systematic literature review and an activation likelihood estimation (ALE) meta-analysis to investigate the actual convergence of functional deviations observed in SUD samples. Results were further supplied by consecutive robustness measures and a post-hoc random-effects meta-analysis of behavioural data.

RESULTS

We identified k = 21 eligible studies for our analysis. The ALE analysis indicated a significant cluster of convergence with its statistical peak in the right anterior insula. Consecutive analyses, however, indicated this result was not robust and susceptible towards publication bias. Additionally, a post-hoc random effects meta-analysis of the behavioural parameters of Go/NoGo and stop-signal paradigms reported by the included studies revealed no significant differences in task performance comparing SUD samples and controls.

CONCLUSION

We discuss that the role of task-based response inhibition may require some refinement as an overarching marker for SUD pathology. Finally, we give a few prospects for future research that should be further explored in this context.

Increase in physiological inhibitory control results in better suppression of unwanted memories

Forgetting or suppressing a memory with unwanted content is just as important as remembering a desirable one. In addition to emphasizing the role of inhibitory control in memory suppression processes, neuropsychological studies have indicated that an intentional inhibition targeting a brain area may exert its inhibitory effects in seemingly unrelated areas through a common inhibitory network. In this study, we aimed to investigate whether the suppression of unwanted memories can be strengthened by recruiting an inhibitory task that can be simultaneously performed with a memory suppression task. Therefore, we manipulated the level of urinary urgency-induced inhibition of participants (N = 180) and test its effect on the suppression of unwanted memories using a Think/No-Think (T/NT) task. The results of our study indicated that individuals with high levels of urinary urgency demonstrated greater memory suppression compared to those with low urinary urgency. Findings and their implications are discussed within the context of cognitive and clinical perspectives, and recommendations are made for future research.

Impaired Non-Selective Response Inhibition in Obsessive-Compulsive Disorder

Two prominent features of obsessive-compulsive disorder (OCD) are the inability to inhibit intrusive thoughts and behaviors and pathological doubt or intolerance of uncertainty. Previous study showed that uncertain context modeled by equiprobable presentation of excitatory (Go) and inhibitory (NoGo) stimuli requires non-selective response inhibition in healthy subjects. In other words, it requires transient global inhibition triggered not only by excitatory stimuli but also by inhibitory stimuli. Meanwhile, it is unknown whether OCD patients show abnormal brain activity of the non-selective response inhibition system. In order to test this assumption, we performed an fMRI study with an equiprobable Go/NoGo task involving fourteen patients with OCD and compared them with 34 healthy controls. Patients with OCD showed pathological slowness in the Go/NoGo task. The non-selective response inhibition system in OCD included all brain areas seen in healthy controls and, in addition, involved the right anterior cingulate cortex (ACC) and the anterior insula/frontal operculum (AIFO). Moreover, a between-group comparison revealed hypoactivation of brain regions within cingulo-opercular and cortico-striato-thalamo-cortical (CSTC) circuits in OCD. Among hypoactivated areas, the right ACC and the right dorsolateral prefrontal cortex (DLPFC) were associated with non-selective inhibition. Furthermore, regression analysis showed that OCD slowness was associated with decreased activation in cingulate regions and two brain areas related to non-selective inhibition: the right DLPFC and the right inferior parietal lobule (IPL). These results suggest that non-selective response inhibition is impaired in OCD, which could be a potential explanation for a relationship between inhibitory deficits and the other remarkable characteristic of OCD known as intolerance of uncertainty.