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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.

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In recent years, chaotic synchronization has received a lot of interest in applications in different fields, including in the design of private and secure communication systems. The purpose of this paper was to achieve the synchronization of the Méndez-Arellano-Cruz-Martínez (MACM) 3D chaotic system coupled in star topology. The MACM electronic circuit is used as chaotic nodes in the communication channels to achieve synchronization in the proposed star network; the corresponding electrical hardware in the slave stages receives the coupling signal from the master node. In addition, a novel application to the digital image encryption process is proposed using the coupled-star-network; and the switching parameter technique is finally used to transmit an image as an encrypted message from the master node to the slave coupled nodes. Finally, the cryptosystem is submitted to statistical tests in order to show the effectiveness in multi-user secure image applications.

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In this work, the problem of master-slave outer synchronization in different inner-outer network topologies is presented. Specifically, the studied inner-outer network topologies are coupled in master-slave configuration, where some particular scenarios concerning inner-outer topologies are addressed in order to disclose a suitable coupling strength to achieve outer synchronization. The novel MACM chaotic system is used as a node in the coupled networks, which presents robustness in its bifurcation parameters. Extensive numerical simulations are presented where the stability of the inner-outer network topologies is analyzed through a master stability function approach.

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Previous fMRI research identified superior temporal sulcus as central integration area for audiovisual stimuli. However, less is known about a general multisensory integration network across senses. Therefore, we conducted activation likelihood estimation meta-analysis with multiple sensory modalities to identify a common brain network. We included 49 studies covering all Aristotelian senses i.e., auditory, visual, tactile, gustatory, and olfactory stimuli. Analysis revealed significant activation in bilateral superior temporal gyrus, middle temporal gyrus, thalamus, right insula, and left inferior frontal gyrus. We assume these regions to be part of a general multisensory integration network comprising different functional roles. Here, thalamus operate as first subcortical relay projecting sensory information to higher cortical integration centers in superior temporal gyrus/sulcus while conflict-processing brain regions as insula and inferior frontal gyrus facilitate integration of incongruent information. We additionally performed meta-analytic connectivity modelling and found each brain region showed co-activations within the identified multisensory integration network. Therefore, by including multiple sensory modalities in our meta-analysis the results may provide evidence for a common brain network that supports different functional roles for multisensory integration.

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Distraction and reinterpretation have been recognized as two different tactics of emotion regulation. As a tactic of attention deployment, distraction involves shifting attention to neutral information or performing a secondary task to distract attention from emotion stimuli of the primary task. Reinterpretation, a representative tactic of cognitive change, was defined as changing the meaning of a situation to enhance or reduce its emotional impact. Thus, there are significant differences between the two processes. We wondered if the neural mechanisms underlying distraction and reinterpretation are different. Even though their neural correlates have been widely studied with functional magnetic resonance imaging (fMRI), few studies were conducted to compare the two tactics directly. Here we conducted an activation likelihood estimation (ALE) meta-analyses to investigate the common or different neural bases of distraction and reinterpretation. Moreover, we also used the meta-analytic connectivity modeling (MACM) to identify the emotion regulation network of distraction and reinterpretation. Overall, we found that the left dorsal lateral prefrontal cortex (DLPFC) was consistently activated during distraction and reinterpretation, whereas the left amygdala and inferior frontal gyrus/ventrolateral prefrontal cortex (VLPFC) were specifically activated during reinterpretation alone. The results indicate that the neural basis of distraction and reinterpretation are similar but not identical. The MACM results showed that distraction and reinterpretation share a common emotion regulation network, including the bilateral DLPFC, the dorsal medial prefrontal cortex, the inferior parietal lobule, the insula, the left (pre) supplementary motor area, the left middle temporal gyrus, and the right superior temporal gyrus. However, that network may subserve different functions when adopting various emotion regulation strategies. In addition, we suggest that the emotion regulation network of the left VLPFC may be a specific regulatory network for reinterpretation.

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Disgust represents a multifaceted defensive-avoidance response. On the behavioral level, the response includes withdrawal and a disgust-specific facial expression. While both serve the avoidance of pathogens, the latter additionally transmits social-communicative information. Given that common and distinct brain representation of the primary defensive-avoidance response (core disgust) and encoding of the social-communicative signal (social disgust) remain debated, we employed neuroimaging meta-analyses to (1) determine brain systems generally engaged in disgust processing, and (2) segregate common and distinct brain systems for core and social disgust. Disgust processing, in general, engaged a bilateral network encompassing the insula, amygdala, occipital and prefrontal regions. Core disgust evoked stronger reactivity in left-lateralized threat detection and defensive response network including amygdala, occipital and frontal regions, while social disgust engaged a right-lateralized superior temporal-frontal network involved in social cognition. Anterior insula, inferior frontal and fusiform regions were commonly engaged during core and social disgust, suggesting a shared neurofunctional basis. We demonstrate a common and distinct neural basis of primary disgust responses and encoding of associated social-communicative signals.

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Discrimination of facial expressions is an elementary function of the human brain. While the way emotions are represented in the brain has long been debated, common and specific neural representations in recognition of facial expressions are also complicated. To examine brain organizations and asymmetry on discrete and dimensional facial emotions, we conducted an activation likelihood estimation meta-analysis and meta-analytic connectivity modelling on 141 studies with a total of 3138 participants. We found consistent engagement of the amygdala and a common set of brain networks across discrete and dimensional emotions. The left-hemisphere dominance of the amygdala and AI across categories of facial expression, but category-specific lateralization of the vmPFC, suggesting a flexibly asymmetrical neural representations of facial expression recognition. These results converge to characteristic activation and connectivity patterns across discrete and dimensional emotion categories in recognition of facial expressions. Our findings provide the first quantitatively meta-analytic brain network-based evidence supportive of the psychological constructionist hypothesis in facial expression recognition.

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Sharing our feelings through content with images and short videos is one main way of expression on social networks. Visual content can affect people's emotions, which makes the task of analyzing the sentimental information of visual content more and more concerned. Most of the current methods focus on how to improve the local emotional representations to get better performance of sentiment analysis and ignore the problem of how to perceive objects of different scales and different emotional intensity in complex scenes. In this paper, based on the alterable scale and multi-level local regional emotional affinity analysis under the global perspective, we propose a multi-level context pyramid network (MCPNet) for visual sentiment analysis by combining local and global representations to improve the classification performance. Firstly, Resnet101 is employed as backbone to obtain multi-level emotional representation representing different degrees of semantic information and detailed information. Next, the multi-scale adaptive context modules (MACM) are proposed to learn the sentiment correlation degree of different regions for different scale in the image, and to extract the multi-scale context features for each level deep representation. Finally, different levels of context features are combined to obtain the multi-cue sentimental feature for image sentiment classification. Extensive experimental results on seven commonly used visual sentiment datasets illustrate that our method outperforms the state-of-the-art methods, especially the accuracy on the FI dataset exceeds 90%.

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Childhood and adolescence represent a time notable for the emergence of many psychiatric disorders, where comorbidity and co-occurrence of symptoms are well-documented. However, it remains unclear whether there exists common brain structural disturbance across psychiatric disorders in youth. Here, we conduct a transdiagnostic meta-analysis of 132 structural neuroimaging experiments in youth consisting of multiple psychiatric diagnoses. Compared to healthy peers, youth psychiatric disorders are characterized by reduced grey matter volume (GMV) of amygdala and lateral orbitofrontal cortex and enhanced GMV of ventromedial prefrontal cortex and precuneus. These four regions were then subjected to functional connectivity and decoding analyses based on healthy participant datasets, allowing for a data-driven quantitative inference on psychophysiological functions. These regions and their networks mapped onto systems implicated in negative valence, positive valence, as well as social and cognitive functioning. Together, our findings are consistent with transdiagnostic models of psychopathology, uncovering common structural disturbance across youth psychiatric disorders, potentially reflecting an intermediate transdiagnostic phenotype in association with broad dimensions of youth psychopathology.

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Background: The human striatum is a heterogeneous structure involved in diverse functional domains that related to distinct striatum subregions. Striatal dysfunction was thought to be a fundamental element in schizophrenia. However, the connectivity pattern of striatum solely based on functional or structural characteristics leads to inconsistent findings in healthy adult and also schizophrenia. This study aims to develop an integrated striatal model and reveal the altered functional connectivity pattern of the striatum in schizophrenia. Methods: Two data-driven approaches, task-dependent meta-analytic connectivity modeling (MACM) and task-independent resting-state functional connectivity (RSFC), were used for seven anatomical connectivity-based striatum subregions to provide an integrated striatal model. Then, RSFC analyses of seven striatal subregions were applied to 45 first-episode schizophrenia (FES) and 27 healthy controls to examine the difference, based on the integrated model, of functional connectivity pattern of striatal subregions. Results: MACM and RSFC results showed that striatum subregions were associated with discrete cortical regions and involved in distinct cognitive processes. Besides, RSFC results overlapped with MACM findings but showed broader distributions. Importantly, significantly reduced functional connectivity was identified between limbic subregion and thalamus, medial prefrontal cortex, anterior cingulate cortex, and insula and also between executive subregions and thalamus, supplementary motor area, and insula in FES. Conclusions: Combing functional and structural connectivity information, this study provides the integrated model of corticostriatal subcircuits and confirms the abnormal functional connectivity of limbic and executive striatum subregions with different networks and thalamus, supporting the important role of the corticostriatal-thalamic loop in the pathophysiology of schizophrenia.

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Skilled visual object and pattern recognition form the basis of many everyday behaviours. The game of chess has often been used as a model case for studying how long-term experience aides in perceiving objects and their spatio-functional interrelations. Earlier research revealed two brain regions, posterior middle temporal gyrus (pMTG) and collateral sulcus (CoS), to be linked to chess experts' superior object and pattern recognition, respectively. Here we elucidated the brain networks these two expertise-related regions are embedded in, employing resting-state functional connectivity analysis and meta-analytic connectivity modelling with the BrainMap database. pMTG was preferentially connected with dorsal visual stream areas and a parieto-prefrontal network for action planning, while CoS was preferentially connected with posterior medial cortex and hippocampus, linked to scene perception, perspective-taking and navigation. Functional profiling using BrainMap meta-data revealed that pMTG was linked to semantic processing as well as inhibition and attention, while CoS was linked to face and shape perception as well as passive viewing. Our findings suggest that pMTG subserves skilled object recognition by mediating the link between object identity and object affordances, while CoS subserves skilled pattern recognition by linking the position of individual objects with typical spatio-functional layouts of their environment stored in memory.

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There is considerable evidence that emotion dysregulation and self-control impairments lead to escalated aggression in populations with psychiatric disorders. However, convergent quantitative evidence on the neural network explaining how aggression arises is still lacking. To address this gap, peak activations extracted from extant functional magnetic resonance imaging (fMRI) studies were synthesized through coordinate-based meta-analyses. A systematic search in the PubMed database was conducted and 26 fMRI studies met the inclusion criteria. Three separate activation likelihood estimation (ALE) meta-analyses were performed on (1) individual differences in trait aggression (TA) studies, (2) individual differences in TA studies examining executive functioning, and (3) elicited aggression (EA) studies across fMRI behavioral paradigms. Ensuing clusters from ALE meta-analyses were further treated as seeds for follow-up investigations on consensus connectivity networks (CCN) delineated from meta-analytic connectivity modeling (MACM) and resting-state functional connectivity (RSFC) to further characterize their physiological functions. Finally, we obtained a data-driven functional characterization of the ensuing clusters and their networks. This approach offers a boarder view of the ensuing clusters using a boarder network perspective. In TA, aberrant brain activations were found only in the right precuneus. Follow-up analyses revealed that the precuneus seed was within the frontal-parietal network (FPN) associated with action inhibition, visuospatial processing and higher-level cognition. With further restricting to only experiments examining executive functioning, convergent evidence was found in the right rolandic operculum (RO), midcingulate cortex (MCC), precentral gyrus (PrG) and precuneus. Follow-up analyses suggested that RO, MCC and PrG may belong to a common cognitive control network, while the MCC seems to be the hub of this network. In EA, we only revealed a convergent region in the left postcentral gyrus. Follow-up CCN analyses and functional characterizations suggested that this region may also belong to the same cognitive control network found in the TA sub-analysis. Our results suggested that escalated aggression arises from abnormal precuneus activities within the FPN, disrupting the recruitment of other large-scale networks such as adaptive cognitive control network. Consequently, failure to recruit such a network results in an inability to generate adaptive responses, increasing the likelihood of acting aggressively.

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The inability to control or inhibit emotional distractors characterizes a range of psychiatric disorders. Despite the use of a variety of task paradigms to determine the mechanisms underlying the control of emotional interference, a precise characterization of the brain regions and networks that support emotional interference processing remains elusive. Here, we performed coordinate-based and functional connectivity meta-analyses to determine the brain networks underlying emotional interference. Paradigms addressing interference processing in the cognitive or emotional domain were included in the meta-analyses, particularly the Stroop, Flanker, and Simon tasks. Our results revealed a consistent involvement of the bilateral dorsal anterior cingulate cortex, anterior insula, left inferior frontal gyrus, and superior parietal lobule during emotional interference. Follow-up conjunction analyses identified correspondence in these regions between emotional and cognitive interference processing. Finally, the patterns of functional connectivity of these regions were examined using resting-state functional connectivity and meta-analytic connectivity modeling. These regions were strongly connected as a distributed system, primarily mapping onto fronto-parietal control, ventral attention, and dorsal attention networks. Together, the present findings indicate that a domain-general neural system is engaged across multiple types of interference processing and that regulating emotional and cognitive interference depends on interactions between large-scale distributed brain networks.

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Purpose: The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM) using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH) (Seeley et al., 2009) by identifying structural and functional covariance in this hippocampal network. Methods: To generate our network model, we used BrainMap's VBM database to perform a region-to-whole-brain (RtWB) meta-analysis of 269 VBM experiments from 165 published studies across a range of 38 psychiatric and neurological diseases reporting hippocampal gray matter density alterations. This step identified 11 significant gray matter foci, or nodes. We subsequently used meta-analytic connectivity modeling (MACM) to define edges of structural covariance between nodes from VBM data as well as functional covariance using the functional task-activation database, also from BrainMap. Finally, we applied a correlation analysis using Pearson's r to assess the similarities and differences between the structural and functional covariance models. Key findings: Our hippocampal RtWB meta-analysis reported consistent and significant structural covariance in 11 key regions. The subsequent structural and functional MACMs showed a strong correlation between HNM nodes with a significant structural-functional covariance correlation of r = .377 (p = .000049). Significance: This novel method of studying network covariance using VBM and functional meta-analytic techniques allows for the identification of generalizable patterns of functional and structural abnormalities pertaining to the hippocampus. In accordance with the NDH, this framework could have major implications in studying and predicting spatial disease patterns using network-based assays.

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The prediction of emotion has been explored in a variety of functional brain imaging and neurophysiological studies. However, an overall picture of the areas involved this process remains unexploited. Here, we quantitatively summarized the published literature on emotional prediction using activation likelihood estimation (ALE) in functional magnetic resonance imaging (fMRI). Furthermore, the current study employed a meta-analytic connectivity modeling (MACM) to map the meta-analytic coactivation maps of regions of interest (ROIs). Our ALE analysis revealed significant convergent activations in some vital brain areas involved in emotional prediction, including the dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), orbitofrontal cortex (OFC) and medial prefrontal cortex (MPFC). For the MACM analysis, we identified that the DLPFC, VLPFC and OFC were the core areas in the coactivation network of emotional prediction. Overall, the results of ALE and MACM indicated that prefrontal brain areas play critical roles in emotional prediction.

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The amygdala is one of the most extensively studied human brain regions and undisputedly plays a central role in many psychiatric disorders. However, an outstanding question is whether connectivity of amygdala subregions, specifically the centromedial (CM), laterobasal (LB) and superficial (SF) nuclei, are modulated by brain state (i.e., task vs. rest). Here, using a multimodal approach, we directly compared meta-analytic connectivity modeling (MACM) and specific co-activation likelihood estimation (SCALE)-derived estimates of CM, LB and SF task-based co-activation to the functional connectivity of these nuclei as assessed by resting state fmri (rs-fmri). Finally, using a preexisting resting state functional connectivity-derived cortical parcellation, we examined both MACM and rs-fmri amygdala subregion connectivity with 17 large-scale networks, to explicitly address how the amygdala interacts with other large-scale neural networks. Analyses revealed strong differentiation of CM, LB and SF connectivity patterns with other brain regions, both in task-dependent and task-independent contexts. All three regions, however, showed convergent connectivity with the right ventrolateral prefrontal cortex (VLPFC) that was not driven by high base rate levels of activation. Similar patterns of connectivity across rs-fmri and MACM were observed for each subregion, suggesting a similar network architecture of amygdala connectivity with the rest of the brain across tasks and resting state for each subregion, that may be modified in the context of specific task demands. These findings support animal models that posit a parallel model of amygdala functioning, but importantly, also modify this position to suggest integrative processing in the amygdala.

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Human neuroimaging methods have provided a number of means by which the connectivity structure of the human brain can be inferred. For instance, correlations in blood-oxygen-level-dependent (BOLD) signal time series are commonly used to make inferences about "functional connectivity." Correlations across samples in structural morphometric measures, such as voxel-based morphometry (VBM) or cortical thickness (CT), have also been used to estimate connectivity, putatively through mutually trophic effects on connected brain areas. In this study, we have compared seed-based connectivity estimates obtained from four common correlational approaches: resting-state functional connectivity (RS-fMRI), meta-analytic connectivity modeling (MACM), VBM correlations, and CT correlations. We found that the two functional approaches (RS-fMRI and MACM) had the best agreement. While the two structural approaches (CT and VBM) had better-than-random convergence, they were no more similar to each other than to the functional approaches. The degree of correspondence between modalities varied considerably across seed regions, and also depended on the threshold applied to the connectivity distribution. These results demonstrate some degrees of similarity between connectivity inferred from structural and functional covariances, particularly for the most robust functionally connected regions (e.g., the default mode network). However, they also caution that these measures likely capture very different aspects of brain structure and function.

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The antisaccade task has been widely used to investigate cognitive action control. While the general network for saccadic eye movements is well defined, the exact location of eye fields within the frontal cortex strongly varies between studies. It is unknown whether this inconsistency reflects spatial uncertainty or is the result of different involvement of subregions for specific aspects of eye movement control. The aim of the present study was to examine functional differentiations within the frontal cortex by integrating results from neuroimaging studies analyzing pro- and antisaccade behavior using meta-analyses. The results provide evidence for a differential functional specialization of neighboring oculomotor frontal regions, with lateral frontal eye fields (FEF) and supplementary eye field (SEF) more often involved in prosaccades while medial FEF and anterior midcingulate cortex (aMCC) revealed consistent stronger involvement for antisaccades. This dissociation was furthermore mirrored by functional connectivity analyses showing that the lateral FEF and SEF are embedded in a motor output network, while medial FEF and aMCC are integrated in a multiple demand network.

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Theories regarding the functional specialization of the hippocampus date back to over a century ago. Two main theories have dominated the field. First, evidence has supported the notion of hemispheric specialization, which appears to be preserved across species. Second, an emergent and mounting set of data has suggested an anterior-posterior neurofunctional gradient. However, no study has examined these theories, and their potential interaction, using objective, robust methodological approaches. Here, we employed an established meta-analytic technique and use ultra-high field, high-resolution functional and structural neuroimaging to examine hippocampal lateralization with consideration for a long-axis differentiation. Data revealed strong support for an evolutionarily preserved hemispheric specialization. Specifically, we found intra- and interhemispheric differences with regard to anterior and posterior functional and structural connectivity, between the right and left hippocampi. For task-independent functional connectivity, we found the right anterior hippocampus to have functional connectivity with a large, distributed network, whereas the left anterior hippocampus demonstrated primarily fronto-limbic connectivity. These patterns were reversed for the posterior segmentations. Not surprisingly, for task-dependent connectivity, we found interhemispheric differences within key ipsilateral structures (i.e., parahippocampal gyrus) for both anterior and posterior segmentations. Furthermore, we identified pivotal neural hubs that share connectivity across behavioral domains, and are supported by structural connectivity (i.e., posterior cingulate cortex). Thus, our data provide evidence for a hemisphere-specific, anterior-posterior specialization of the hippocampal formation.

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Melanoma cell adhesion molecule (MACM) has been reported in many studies as a novel bio-marker for its prognosis value in cancers. But the prognosis significance of MACM expression in cancer remains inconclusive. Therefore, we conducted a system review and meta-analysis to assess its prognosis value in cancers. A systematic search through Pubmed, EMBASE and Cochran Library database was conducted. Hazard Ratios (HRs) and 95% confidence intervals (CIs) were used to evaluate the prognosis value of MACM expression. Eleven studies with 2657 cases were included after sorting out 462 articles for this meta-analysis. The results of the fixed-model depending on the heterogeneity in studies demonstrated that MACM expression was significantly associated with overall survival (OS) in cancer (HR=2.84, 95% CI: 1.10-7.31, P<0.00001). Furthermore, subgroup analysis indicated that high expressed MACM predicted a poor OS in both Asian (HR=2.52, 95% CI: 1.80-3.52, P<0.00001) and Caucasian (HR=2.40, 95% CI: 2.01-2.88, P<0.00001). In conclusion, high expression of MACM was significantly associated with a poor prognostic outcome in cancer. MACM can be regarded as a novel bio-marker in different types of cancers and can be used to evaluate the prognosis of therapeutic effect during clinical practices.