Meta-analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional- towards more network-based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta-analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta-analytic groupings of experiments demonstrating whole-brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta-analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta-analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large-scale brain networks.

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Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may answer broader questions regarding how naturalistic cognition is biologically distributed throughout the brain. We applied an unbiased, data-driven, meta-analytic approach that uses k-means clustering to identify core brain networks engaged across the naturalistic functional neuroimaging literature. Functional decoding allowed us to, then, delineate how information is distributed between these networks throughout the execution of dynamical cognition in realistic settings. This analysis revealed six recurrent patterns of brain activation, representing sensory, domain-specific, and attentional neural networks that support the cognitive demands of naturalistic paradigms. Although gaps in the literature remain, these results suggest that naturalistic fMRI paradigms recruit a common set of networks that allow both separate processing of different streams of information and integration of relevant information to enable flexible cognition and complex behavior.

Availability of data and material: Unthresholded and thresholded statistical maps of the image-based meta-analysis results are available for download from NeuroVault (https://identifiers.org/neurovault.collection:8448). Code availability: The NiMARE software used for the image-based meta-analysis results reported in this manuscript is available for download (https://nimare.readthedocs.io/en/latest/index.html; https://github.com/neurostuff/NiMARE). Author Contributions: HvEV, STW, MCR, and ARL conceived of and designed the study. HvEV and STW performed the literature search and collected the published SPMs. TS and MCR contributed scripts, performed meta-analyses, and drafted the figures. HvEV and STW cowrote the manuscript.

Understanding how students learn is crucial for helping them succeed. We examined brain function in 107 undergraduate students during a task known to be challenging for many students—physics problem solving—to characterize the underlying neural mechanisms and determine how these support comprehension and proficiency. Further, we applied module analysis to response distributions, defining groups of students who answered by using similar physics conceptions, and probed for brain differences linked with different conceptual approaches. We found that integrated executive, attentional, visual motion, and default mode brain systems cooperate to achieve sequential and sustained physics-related cognition. While accuracy alone did not predict brain function, dissociable brain patterns were observed when students solved problems by using different physics conceptions, and increased success was linked to conceptual coherence. Our analyses demonstrate that episodic associations and control processes operate in tandem to support physics reasoning, offering potential insight to support student learning.

The habenula, an epithalamic nucleus involved in reward and aversive processing, may contribute to negative reinforcement mechanisms maintaining nicotine use. We used a performance feedback task that differentially activates the striatum and habenula and administered nicotine and varenicline (versus placebos) to overnight-abstinent smokers and nonsmokers to delineate feedback-related functional brain alterations both as a function of smoking trait (smokers versus nonsmokers) and drug administration state (drug versus placebo). Smokers showed less striatal responsivity to positive feedback, an alteration not mitigated by drug administration, but rather correlated with trait-level addiction severity. Conversely, nicotine administration reduced habenula activity following both positive and negative feedback among abstinent smokers, but not nonsmokers, and increased habenula activity among smokers correlated with elevated state-level tobacco cravings. These outcomes highlight a dissociation between neurobiological processes linked with the dependence severity trait and the nicotine withdrawal state. Interventions simultaneously targeting both aspects may improve currently poor cessation outcomes.

The current state of label conventions used to describe brain networks related to executive functions is highly inconsistent, leading to confusion among researchers regarding network labels. Visually similar networks are referred to by different labels, yet these same labels are used to distinguish networks within studies. We performed a literature review of fMRI studies and identified nine frequently-used labels that are used to describe topographically or functionally similar neural networks: central executive network (CEN), cognitive control network (CCN), dorsal attention network (DAN), executive control network (ECN), executive network (EN), frontoparietal network (FPN), working memory network (WMN), task positive network (TPN), and ventral attention network (VAN). Our aim was to meta-analytically determine consistency of network topography within and across these labels. We hypothesized finding considerable overlap in the spatial topography among the neural networks associated with these labels. An image-based meta-analysis was performed on 166 individual statistical maps (SPMs) received from authors of 72 papers listed on PubMed. Our results indicated that there was very little consistency in the SPMs labeled with a given network name. We identified four clusters of SPMs representing four spatially distinct executive function networks. We provide recommendations regarding label nomenclature and propose that authors looking to assign labels to executive function networks adopt this template set for labeling networks.

Drug and natural cue-reactivityHill-Bowen et al. ABSTRACTBackground: The cue-reactivity paradigm is a widely adopted neuroimaging probe assessing brain activity linked to attention, memory, emotion, and reward processing associated with the presentation of appetitive stimuli. Lacking, is the apperception of more precise brain regions, neurocircuits, and mental operations comprising cue-reactivity's multi-elemental nature. To resolve such complexities, we employed emergent meta-analytic techniques to enhance insight into drug and natural cue-reactivity in the brain.Methods: Operating from this perspective, we first conducted multiple coordinate-based metaanalyses to define common and distinct brain regions showing convergent activation across studies involving drug-related and natural-reward cue-reactivity paradigms. In addition, we examined the activation profiles of each convergent brain region linked to cue-reactivity as seeds in taskdependent and task-independent functional connectivity analyses. Using methods to cluster regions of interest, we categorized cue-reactivity into cliques, or sub-networks, based on the functional similarities between regions. Cliques were further classified with psychological constructs.Results: We identified a total of 164 peer-reviewed articles: 108 drug-related, and 56 naturalreward. When considering cue-reactivity collectively, across both drug and natural studies, activity convergence was observed in the dorsal striatum, limbic, insula, parietal, occipital, and temporal regions. Common convergent neural activity between drug and natural cue-reactivity was observed in the caudate, amygdala, thalamus, cingulate, and temporal regions. Drug distinct convergence was observed in the putamen, cingulate, and temporal regions, while natural distinct convergence was observed in the caudate, parietal, occipital, and frontal regions. We seeded identified cuereactivity regions in meta-analytic connectivity modeling and resting-state functional connectivity analyses. Consensus hierarchical clustering of both connectivity analyses identified six distinct cliques that were further functionally characterized using the BrainMap and Neurosynth databases.Conclusions: We examined the multifaceted nature of cue-reactivity and decomposed this construct into six elements of visual, executive function, sensorimotor, salience, emotion, and selfreferential processing. Further, we demonstrated that these elements are supported by perceptual, sensorimotor, tripartite, and affective networks, which are essential to understanding the neural mechanisms involved in the development and or maintenance of addictive disorders.