The collection of recommendations and guidelines for conducting and reporting neuroimaging meta-analyses, called \"Ten simple rules for neuroimaging meta-analysis\" by Müller et al., has been published for a few years. Here, the papers citing this reference were examined to evaluate the rationale of the quotations and what quotation errors existed. In May 2023, an online query via Scopus identified 386 papers citing this reference, 2 of which were inaccessible. The resultant 384 papers were checked to identify the total number of quotations to the reference, the exact quotations, which of the ten recommendations/rules was concerned by each quotation, and if any quotation error existed. Results found that the reference by Müller et al. were quoted 804 times by the 384 papers, meaning an average of 2.1 quotations per paper. Out of the 804 quotations, the three rules that the researchers most frequently referred were the power of the meta-analysis (Rule #2, 14.1%), the consistency of the search coverage and reference space (Rule #4, 13.8%), and the statistical threshold (Rule #8, 10.2%). Overall, 63 quotations from 51 papers contained some errors. In other words, 7.8% (63/804) of the quotations contained errors and they involved 13.3% (51/384) of the papers. The commonest quotation errors were dealing with a failure to substantiate the assertion, unrelated to the assertion, and oversimplification of the original notion. Some notable quotation error examples were to quote Müller et al. to substantiate the assertion of having at least 10 datasets to be considered to have adequate power for ES-SDM meta-analysis (no such recommendation), and having a misquoted primary cluster-forming threshold of p < 0.05 or p < 0.005 (should be p < 0.001). The neuroscience community should be cautious and double-check the accuracy of assertions, even with a quotation.

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain\'s large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show \"core\" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.

Although there is general consensus that altered brain structure and function underpins addictive disorders, clinicians working in addiction treatment rarely incorporate neuroscience-informed approaches into their practice. We recently launched the Neuroscience Interest Group within the International Society of Addiction Medicine (ISAM-NIG) to promote initiatives to bridge this gap. This article summarizes the ISAM-NIG key priorities and strategies to achieve implementation of addiction neuroscience knowledge and tools for the assessment and treatment of substance use disorders. We cover two assessment areas: cognitive assessment and neuroimaging, and two interventional areas: cognitive training/remediation and neuromodulation, where we identify key challenges and proposed solutions. We reason that incorporating cognitive assessment into clinical settings requires the identification of constructs that predict meaningful clinical outcomes. Other requirements are the development of measures that are easily-administered, reliable, and ecologically-valid. Translation of neuroimaging techniques requires the development of diagnostic and prognostic biomarkers and testing the cost-effectiveness of these biomarkers in individualized prediction algorithms for relapse prevention and treatment selection. Integration of cognitive assessments with neuroimaging can provide multilevel targets including neural, cognitive, and behavioral outcomes for neuroscience-informed interventions. Application of neuroscience-informed interventions including cognitive training/remediation and neuromodulation requires clear pathways to design treatments based on multilevel targets, additional evidence from randomized trials and subsequent clinical implementation, including evaluation of cost-effectiveness. We propose to address these challenges by promoting international collaboration between researchers and clinicians, developing harmonized protocols and data management systems, and prioritizing multi-site research that focuses on improving clinical outcomes.

The false consensus effect (FCE) - the tendency to (erroneously) project our attitudes and opinions onto others - is an enduring bias in social reasoning with important societal implications. In this fMRI investigation, we examine the neural correlates of within-subject variation in consensus bias on a variety of social and political issues. Bias demonstrated a strong association with activity in brain regions implicated in self-related cognition, mentalizing, and valuation. Importantly, however, recruitment of these regions predicted consensus bias only in the presence of social disconfirmation, in the form of feedback discrepant with participants\' own attitudes. These results suggest that the psychological and neural mechanisms underlying the tendency to project attitudes onto others are crucially moderated by motivational factors, including the desire to affirm the normativity of one\'s own position. This research complements social psychological theorizing about the factors contributing to the FCE, and further emphasizes the role of motivated cognition in social reasoning.

Information that is shared widely can profoundly shape society. Evidence from neuroimaging suggests that activity in the ventromedial prefrontal cortex (vmPFC), a core region of the brain\'s valuation system tracks with this sharing. However, the mechanisms linking vmPFC responses in individuals to population behavior are still unclear. We used a multilevel brain-as-predictor approach to address this gap, finding that individual differences in how closely vmPFC activity corresponded with population news article sharing related to how closely its activity tracked with social consensus about article value. Moreover, how closely vmPFC activity corresponded with population behavior was linked to daily life news experience: frequent news readers tended to show high vmPFC across all articles, whereas infrequent readers showed high vmPFC only to articles that were more broadly valued and heavily shared. Using functional connectivity analyses, we found that superior tracking of consensus value was related to decreased connectivity of vmPFC with a dorsolateral PFC region associated with controlled processing. Taken together, our results demonstrate variability in the brain\'s capacity to track crowd wisdom about information value, and suggest (lower levels of) stimulus experience and vmPFC-dlPFC connectivity as psychological and neural sources of this variability.

Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely P < 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parameters for Bayesian inference and how to interpret the results, such as Bayes factors, from the inference. We also discuss the practical and philosophical benefits of Bayesian inference and directions for future research.

Biological sex influences cigarette smoking behavior. More men than women smoke, but women have a harder time quitting. Sex differences in smoking cue (SC) reactivity may underlie such behavioral differences. However, the influence of sex on brain reactivity to SCs has yielded inconsistent findings suggesting the need for continued study. Here, we investigated the effect of sex on SC reactivity across two sites using different imaging modalities and SC stimulus types.
Pseudo-continuous arterial spin-labeled (pCASL) perfusion functional magnetic resonance imaging (fMRI) was used to assess brain responses to SC versus non-SC videos in 40 smokers (23 females) at the University of Pennsylvania. BOLD fMRI was used to assess brain responses to SC versus non-SC still images in 32 smokers (18 females) at McLean Hospital. Brain reactivity to SCs was compared between men and women and was correlated with SC-induced craving.
In both cohorts, males showed higher SC versus non-SC reactivity compared to females in reward-related brain regions (i.e., ventral striatum/ventral pallidum, ventral medial prefrontal cortex). Brain activation during SC versus non-SC exposure correlated positively with SC-induced subjective craving in males, but not females.
The current work provides much needed replication and validation of sex differences in SC-reactivity. These findings also add to a body of literature showing that men have greater reward-related brain activation to drug cues across drug classes. Such sex differences confirm the need to consider sex not only when evaluating SC-reactivity but when examining nicotine dependence etiology and treatment.

Decoding, i.e. prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders\' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didactic overview of the relevant theoretical considerations. Practical aspects are highlighted with an extensive empirical study of the common decoders in within- and across-subject predictions, on multiple datasets -anatomical and functional MRI and MEG- and simulations. Theory and experiments outline that the popular \"leave-one-out\" strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred. Experiments outline the large error bars of cross-validation in neuroimaging settings: typical confidence intervals of 10%. Nested cross-validation can tune decoders\' parameters while avoiding circularity bias. However we find that it can be favorable to use sane defaults, in particular for non-sparse decoders.

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package \"UNCLES\" available on http://cran.r-project.org/web/packages/UNCLES/index.html .

The majority of studies which have aimed to identify cognitive and motivational factors at play in ADHD have investigated cognitive-control processes and reinforcement effects in isolation. Notably, in recent years, the interaction between these two processes has been increasingly examined. Here, we aimed to provide a comprehensive and critical review of the behavioral and functional neuroimaging studies that have investigated reinforcement effects on inhibitory control in ADHD. The findings of our meta-analyses show that reinforcement can normalize inhibitory control in children and adolescents with ADHD to the baseline level of controls. Furthermore, the data suggests that inhibitory control may improve to a larger extent in youth with ADHD compared with controls, as a function of reinforcement. Based on (1) this review and meta-analyses, (2) functional neuroimaging studies in healthy populations, and (3) existing ADHD and neurobiological models of dual processes, we propose specific guidelines for future research, which are anticipated to further elucidate processes underlying impulsive behavior associated with ADHD.