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

Yan, Haifeng; Lau, Way Kwok‐Wai; Eickhoff, Simon B.; Long, Jixin; Song, Xiaoqi; Wang, Chanyu; Zhao, Jingbo; Feng, Xiangang; Huang, Ruiwang; Wang, Maosheng; Zhang, Xiaoyuan; Zhang, Ruibin

doi:10.1016/j.pnpbp.2022.110618

Cited by 7 publications

(2 citation statements)

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“…This need activates the prefrontal areas, and these areas send inhibitory signals to the memory‐related areas to inhibit their functions. (Anderson & Hulbert, 2021; Benoit & Anderson, 2012; Gagnepain et al, 2017; Yan et al, 2022). Schmitz et al (2017) revealed increased activation in the prefrontal areas during NT trials and that this activation is accompanied by hippocampal deactivation.…”

Section: Discussionmentioning

confidence: 99%

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

Gündüz

Çetinkaya

2023

British J of Psychology

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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.

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Section: Discussionmentioning

confidence: 99%

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

Gündüz

Çetinkaya

2023

British J of Psychology

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“…The rAI is an often-replicated meta-analytically derived area to be involved in the performances of both GNGT and SST (Swick et al 2011 ; Criaud and Boulinguez 2013 ; Sebastian et al 2013 ; Zhang et al 2017 ; Hung et al 2018 ; Puiu et al 2020 ). However, the rAI has also been found to be one of the most consistently reported meta-analytic findings of functional aberrations across a vast variety of psychiatric disorders during cognitive and inhibitory control (McTeague et al 2017 ; Yan et al 2022 ). This suggests that the cluster of convergence we identified might be difficult to disentangle from comorbid conditions.…”

Section: Discussionmentioning

confidence: 99%

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

Fascher,

Nowaczynski,

Spindler

et al. 2023

Psychopharmacology

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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.

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Aggregated Pattern Classification Method for improving neural disorder stage detection

Anjum,

Shahab,

Ahmad

et al. 2024

Brain and Behavior

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BackgroundNeurological 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.MethodThe 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.ResultsThe 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.ConclusionThe 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.

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

Cited by 7 publications

References 82 publications

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

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

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

Aggregated Pattern Classification Method for improving neural disorder stage detection

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