Sensory attenuation refers to the reduction in sensory intensity resulting from self-initiated actions compared to stimuli initiated externally. A classic example is scratching oneself without feeling itchy. This phenomenon extends across various sensory modalities, including visual, auditory, somatosensory, and nociceptive stimuli. The internal forward model proposes that during voluntary actions, an efferent copy of the action command is sent out to predict sensory feedback. This predicted sensory feedback is then compared with the actual sensory feedback, leading to the suppression or reduction of sensory stimuli originating from self-initiated actions. To further elucidate the neural mechanisms underlying sensory attenuation effect, we conducted an extensive meta-analysis of functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies. Utilizing activation likelihood estimation (ALE) analysis, our results revealed significant activations in a prominent cluster encompassing the right superior temporal gyrus (rSTG), right middle temporal gyrus (rMTG), and right insula when comparing external-generated with self-generated conditions. Additionally, significant activation was observed in the right anterior cerebellum when comparing self-generated to external-generated conditions. Further analysis using meta-analytic connectivity modeling (MACM) unveiled distinct brain networks co-activated with the rMTG and right cerebellum, respectively. Based on these findings, we propose that sensory attenuation arises from the suppression of reflexive inputs elicited by self-initiated actions through the internal forward modeling of a cerebellum-centered action prediction network, enabling the \"sensory conflict detection\" regions to effectively discriminate between inputs resulting from self-induced actions and those originating externally.

Cognitive control plays a pivotal role in guiding human goal-directed behavior, and revealing its lifespan trajectory is crucial for optimizing cognitive functioning at different ages, especially for stages of rapid development and decline. While existing studies have shed light on the inverted U-shaped trajectory of cognitive control function both behaviorally and anatomically, little is known about the corresponding changes in functional brain activation with age. To bridge this gap, we conducted a comprehensive meta-analysis of 129 neuroimaging studies using conflict tasks, encompassing 3,388 participants whose age spanned from 5 to 85 years old. We applied the seed-based d mapping (SDM), generalized additive model (GAM) and model comparison approaches to investigate age-related changes of brain activity, chart the lifespan trajectories and pinpoint peaks of cognitive control brain activity. The present study have three major findings: 1) The inverted U-shaped lifespan trajectory is the predominant pattern; 2) Cognitive control related brain regions exhibit heterogeneous lifespan trajectories: the frontoparietal control network (such as the inferior frontal gyrus and inferior parietal lobule) follows inverted U-shaped trajectories, peaking between 24 and 41 years, while the dorsal attention network (such as the frontal eye field and superior parietal lobule) demonstrates flatter trajectories with age; 3) Both the youth and the elderly show weaker brain activities and greater left laterality than young adults. These results collectively reveal the lifespan trajectories of cognitive control, highlighting heterogeneous fluctuations in brain networks with age.

The neurobiological underpinnings of insomnia disorder (ID) are still poorly understood. A previous meta-analysis conducted by our research group in 2018 revealed no consistent regional alterations based on the limited number of eligible studies. Given the number of studies published during the last few years, we revisited the meta-analysis to provide an update to the field. Following the best-practice guidelines for conducting neuroimaging meta-analyses, we searched several databases (PubMed, Web of Science, and BrainMap) and identified 39 eligible structural and functional studies, reporting coordinates reflecting significant group differences between ID patients and healthy controls. A significant convergent regional alteration in the subgenual anterior cingulate cortex (sgACC) was observed using the activation likelihood estimation algorithm. Behavioural decoding using the BrainMap database indicated that this region is involved in fear-related emotional and cognitive processing. The sgACC showed robust task-based co-activation in meta-analytic connectivity modelling and task-free functional connectivity in a resting-state functional connectivity analysis with the main hubs of the salience and default mode networks, including the posterior cingulate cortex and dorsal ACC, amygdala, hippocampus, and medial prefrontal cortex. Collectively, the findings from this large-scale meta-analysis suggest a critical role of the sgACC in the pathophysiology of ID.

Despite remarkable success in identifying genetic risk factors for depression, there are still open questions about the exact genetic mechanisms underlying certain disease phenotypes, such as brain structural and functional impairments.
Comprehensive multi-modal neuroimaging meta-analyses were conducted to examine changes in brain structure and function in drug-naive first-episode patients with major depressive disorder (DF-MDD). Combined with the Allen Human Brain Atlas, transcriptome-neuroimaging spatial association analyses were performed to identify genes whose expression related to these brain structural and functional changes, followed by a range of gene functional signature analyses.
Meta-analyses revealed gray matter atrophy in the insula, temporal pole, cerebellum and postcentral gyrus, and a complex pattern of hyper-function in the temporal pole and hypo-function in the cuneus/precuneus, angular gyrus and lingual gyrus in DF-MDD. Moreover, these brain structural and functional changes were spatially associated with the expression of 1194 and 1733 genes, respectively. Importantly, there were commonalities and differences in the two gene sets and their functional signatures including functional enrichment, specific expression, behavioral relevance, and constructed protein-protein interaction networks.
The results merit further verification using a large sample of DF-MDD.
Our findings not only corroborate the polygenic nature of depression, but also suggest common and distinct genetic modulations of brain structural and functional impairments in this disorder.

In recent neuroimaging studies, threshold-free cluster enhancement (TFCE) gained popularity as a sophisticated thresholding method for statistical inference. It was shown to feature higher sensitivity than the frequently used approach of controlling the cluster-level family-wise error (cFWE) and it does not require setting a cluster-forming threshold at voxel level. Here, we examined the applicability of TFCE to a widely used method for coordinate-based neuroimaging meta-analysis, Activation Likelihood Estimation (ALE), by means of large-scale simulations. We created over 200,000 artificial meta-analysis datasets by independently varying the total number of experiments included and the amount of spatial convergence across experiments. Next, we applied ALE to all datasets and compared the performance of TFCE to both voxel-level and cluster-level FWE correction approaches. All three multiple-comparison correction methods yielded valid results, with only about 5% of the significant clusters being based on spurious convergence, which corresponds to the nominal level the methods were controlling for. On average, TFCE\'s sensitivity was comparable to that of cFWE correction, but it was slightly worse for a subset of parameter combinations, even after TFCE parameter optimization. cFWE yielded the largest significant clusters, closely followed by TFCE, while voxel-level FWE correction yielded substantially smaller clusters, showcasing its high spatial specificity. Given that TFCE does not outperform the standard cFWE correction but is computationally much more expensive, we conclude that employing TFCE for ALE cannot be recommended to the general user.

The uniqueness of neural processes between allocentric and egocentric spatial coding has been controversial. The distinctive paradigms used in previous studies for manipulating spatial coding could have attributed for the inconsistent results. This study was aimed to generate converging evidence from previous functional brain imaging experiments for collating neural substrates associated with these two types of spatial coding. An additional aim was to test whether test-taking processes would have influenced the results. We obtained coordinate-based functional neuroimaging data for 447 subjects and performed activation likelihood estimation (ALE) meta-analysis. Among the 28 experiments, the results indicate two common clusters of convergence. They were the right precuneus and the right superior frontal gyrus as parts of the parieto-frontal circuit. Between-type differences were in the parieto-occipital circuit, with allocentric showing convergence in the superior occipital gyrus (SOG) cluster compared with egocentric showing convergence in the middle occipital gyrus (MOG) cluster. Task-specific influences were only found in allocentric spatial coding. Spatial judgment-oriented tasks seem to increase the demands on manipulating spatial relationships among the visual objects, while spatial navigation tasks seem to increase the demands on maintaining object representations. Our findings address the theoretical controversies on spatial coding that both the allocentric and egocentric types are common in their processes mediated by the parieto-frontal network, while unique and additional processes in the allocentric type are mediated by the parieto-occipital network. The positive results on possible task-specific confound offer insights into the future design of spatial tasks for eliciting spatial coding processes.

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.

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how \"parent\" functional brain systems decompose into constituent \"child\" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

Does \"the motor system\" play \"a role\" in speech perception? If so, where, how, and when? We conducted a systematic review that addresses these questions using both qualitative and quantitative methods. The qualitative review of behavioural, computational modelling, non-human animal, brain damage/disorder, electrical stimulation/recording, and neuroimaging research suggests that distributed brain regions involved in producing speech play specific, dynamic, and contextually determined roles in speech perception. The quantitative review employed region and network based neuroimaging meta-analyses and a novel text mining method to describe relative contributions of nodes in distributed brain networks. Supporting the qualitative review, results show a specific functional correspondence between regions involved in non-linguistic movement of the articulators, covertly and overtly producing speech, and the perception of both nonword and word sounds. This distributed set of cortical and subcortical speech production regions are ubiquitously active and form multiple networks whose topologies dynamically change with listening context. Results are inconsistent with motor and acoustic only models of speech perception and classical and contemporary dual-stream models of the organization of language and the brain. Instead, results are more consistent with complex network models in which multiple speech production related networks and subnetworks dynamically self-organize to constrain interpretation of indeterminant acoustic patterns as listening context requires.

We performed a quantitative meta-analysis of functional neuroimaging studies to identify brain areas which are commonly engaged in social and visuo-spatial perspective taking. Specifically, we compared brain activation for visual-perspective taking to activation for false belief reasoning, which requires awareness of perspective to understand someone\'s mistaken belief about the world which contrasts with reality. In support of a previous account by Perner and Leekam (2008), our meta-analytic conjunction analysis found common activation for false belief reasoning and visual perspective taking in the left but not the right dorsal temporo-parietal junction (TPJ). This fits with the idea that the left dorsal TPJ is responsible for representing different perspectives in a domain-general fashion. Moreover, our conjunction analysis found activation in the precuneus and the left middle occipital gyrus close to the putative Extrastriate Body Area (EBA). The precuneus is linked to mental-imagery which may aid in the construction of a different perspective. The EBA may be engaged due to imagined body-transformations when another\'s viewpoint is adopted.