Embodying Time in the Brain: A Multi-Dimensional Neuroimaging Meta-Analysis of 95 Duration Processing Studies.

Time is an omnipresent aspect of almost everything we experience internally or in the external world. The experience of time occurs through such an extensive set of contextual factors that, after decades of research, a unified understanding of its neural substrates is still elusive. In this study, following the recent best-practice guidelines, we conducted a coordinate-based meta-analysis of 95 carefully-selected neuroimaging papers of duration processing. We categorized the included papers into 14 classes of temporal features according to six categorical dimensions. Then, using the activation likelihood estimation (ALE) technique we investigated the convergent activation patterns of each class with a cluster-level family-wise error correction at p < 0.05. The regions most consistently activated across the various timing contexts were the pre-SMA and bilateral insula, consistent with an embodied theory of timing in which abstract representations of duration are rooted in sensorimotor and interoceptive experience, respectively. Moreover, class-specific patterns of activation could be roughly divided according to whether participants were timing auditory sequential stimuli, which additionally activated the dorsal striatum and SMA-proper, or visual single interval stimuli, which additionally activated the right middle frontal and inferior parietal cortices. We conclude that temporal cognition is so entangled with our everyday experience that timing stereotypically common combinations of stimulus characteristics reactivates the sensorimotor systems with which they were first experienced.

The neural signature of reality-monitoring: A meta-analysis of functional neuroimaging studies.

Distinguishing imagination and thoughts from information we perceived from the environment, a process called reality-monitoring, is important in everyday situations. Although reality monitoring seems to overlap with the concept of self-monitoring, which allows one to distinguish self-generated actions or thoughts from those generated by others, the two concepts remain largely separate cognitive domains and their common brain substrates have received little attention. We investigated the brain regions involved in these two cognitive processes and explored the common brain regions they share. To do this, we conducted two separate coordinate-based meta-analyses of functional magnetic resonance imaging studies assessing the brain regions involved in reality- and self-monitoring. Few brain regions survived threshold-free cluster enhancement family-wise multiple comparison correction (p < .05), likely owing to the small number of studies identified. Using uncorrected statistical thresholds recommended by Signed Differential Mapping with Permutation of Subject Images, the meta-analysis of reality-monitoring studies (k = 9 studies including 172 healthy subjects) revealed clusters in the lobule VI of the cerebellum, the right anterior medial prefrontal cortex and anterior thalamic projections. The meta-analysis of self-monitoring studies (k = 12 studies including 192 healthy subjects) highlighted the involvement of a set of brain regions including the lobule VI of the left cerebellum and fronto-temporo-parietal regions. We showed with a conjunction analysis that the lobule VI of the cerebellum was consistently engaged in both reality- and self-monitoring. The current findings offer new insights into the common brain regions underlying reality-monitoring and self-monitoring, and suggest that the neural signature of the self that may occur during self-production should persist in memories.

In search of convergent regional brain abnormality in cognitive emotion regulation: A transdiagnostic neuroimaging meta-analysis.

Ineffective use of adaptive cognitive strategies (e.g., reappraisal) to regulate emotional states is often reported in a wide variety of psychiatric disorders, suggesting a common characteristic across different diagnostic categories. However, the extent of shared neurobiological impairments is incompletely understood. This study, therefore, aimed to identify the transdiagnostic neural signature of disturbed reappraisal using the coordinate-based meta-analysis (CBMA) approach. Following the best-practice guidelines for conducting neuroimaging meta-analyses, we systematically searched PubMed, ScienceDirect, and Web of Science databases and tracked the references. Out of 1,608 identified publications, 32 whole-brain neuroimaging studies were retrieved that compared brain activation in patients with psychiatric disorders and healthy controls during a reappraisal task. Then, the reported peak coordinates of group comparisons were extracted and several activation likelihood estimation (ALE) analyses were performed at three hierarchical levels to identify the potential spatial convergence: the global level (i.e., the pooled analysis and the analyses of increased/decreased activations), the experimental-contrast level (i.e., the analyses of grouped data based on the regulation goal, stimulus valence, and instruction rule) and the disorder-group level (i.e., the analyses across the experimental-contrast level focused on increasing homogeneity of disorders). Surprisingly, none of our analyses provided significant convergent findings. This CBMA indicates a lack of transdiagnostic convergent regional abnormality related to reappraisal task, probably due to the complex nature of cognitive emotion regulation, heterogeneity of clinical populations, and/or experimental and statistical flexibility of individual studies.

Common abnormality of gray matter integrity in substance use disorder and obsessive-compulsive disorder: A comparative voxel-based meta-analysis.

The objective of the current study is to determine robust transdiagnostic brain structural markers for compulsivity by capitalizing on the increasing number of case-control studies examining gray matter volume (GMV) alterations in substance use disorders (SUD) and obsessive-compulsive disorder (OCD). Voxel-based meta-analysis within the individual disorders and conjunction analysis were employed to reveal common GMV alterations between SUDs and OCD. Meta-analytic coordinates and signed brain volumetric maps determining directed (reduced/increased) GMV alterations between the disorder groups and controls served as the primary outcome. The separate meta-analysis demonstrated that SUD and OCD patients exhibited widespread GMV reductions in frontocortical regions including prefrontal, cingulate, and insular. Conjunction analysis revealed that the left inferior frontal gyrus (IFG) consistently exhibited decreased GMV across all disorders. Functional characterization suggests that the IFG represents a core hub in the cognitive control network and exhibits bidirectional (Granger) causal interactions with the striatum. Only OCD showed increased GMV in the dorsal striatum with higher changes being associated with more severe OCD symptomatology. Together the findings demonstrate robustly decreased GMV across the disorders in the left IFG, suggesting a transdiagnostic brain structural marker. The functional characterization as a key hub in the cognitive control network and casual interactions with the striatum suggest that deficits in inhibitory control mechanisms may promote compulsivity and loss of control that characterize both disorders.

BACON: A tool for reverse inference in brain activation and alteration.

Over the past decades, powerful MRI-based methods have been developed, which yield both voxel-based maps of the brain activity and anatomical variation related to different conditions. With regard to functional or structural MRI data, forward inferences try to determine which areas are involved given a mental function or a brain disorder. A major drawback of forward inference is its lack of specificity, as it suggests the involvement of brain areas that are not specific for the process/condition under investigation. Therefore, a different approach is needed to determine to what extent a given pattern of cerebral activation or alteration is specifically associated with a mental function or brain pathology. In this study, we present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived-maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology.

Grey matter alterations in restless legs syndrome: A coordinate-based meta-analysis.

Brain structural abnormalities in idiopathic restless legs syndrome have long been debated. Voxel-based morphometry is an objective structural magnetic resonance imaging technique to investigate regional grey matter volume or density differences between groups. In the last decade, voxel-based morphometry studies have exhibited inconsistent and conflicting findings regarding the presence and localization of brain grey matter alterations in restless legs syndrome. We therefore conducted a coordinate-based meta-analysis to quantitatively examine whether there were consistent grey matter findings in restless legs syndrome using the latest algorithms, seed-based d mapping with permutation of subject images. We included 12 voxel-based morphometry studies (13 datasets, 375 patients and 385 healthy controls). Our coordinate-based meta-analysis did not identify evidence of consistent grey matter alterations in restless legs syndrome. Grey matter alterations via voxel-based morphometry analysis are not therefore recommended to be used as a reliable surrogate neuroimaging marker for restless legs syndrome. This lack of consistency may be attributed to differences in sample size, genetics, gender distribution and age at onset, clinical heterogeneity (clinical course, anatomical distribution of symptoms, disease severity, disease duration, abnormal sensory profiles and comorbidity), and variations in imaging acquisition, data processing and statistical strategies. Longitudinal studies with multimodal neuroimaging techniques are needed to determine whether structural changes are dynamic and secondary to functional abnormalities.

Is Migraine Associated to Brain Anatomical Alterations? New Data and Coordinate-Based Meta-analysis.

A growing number of studies investigate brain anatomy in migraine using voxel- (VBM) and surface-based morphometry (SBM), as well as diffusion tensor imaging (DTI). The purpose of this article is to identify consistent patterns of anatomical alterations associated with migraine. First, 19 migraineurs without aura and 19 healthy participants were included in a brain imaging study. T1-weighted MRIs and DTI sequences were acquired and analyzed using VBM, SBM and tract-based spatial statistics. No significant alterations of gray matter (GM) volume, cortical thickness, cortical gyrification, sulcus depth and white-matter tract integrity could be observed. However, migraineurs displayed decreased white matter (WM) volume in the left superior longitudinal fasciculus. Second, a systematic review of the literature employing VBM, SBM and DTI was conducted to investigate brain anatomy in migraine. Meta-analysis was performed using Seed-based d Mapping via permutation of subject images (SDM-PSI) on GM volume, WM volume and cortical thickness data. Alterations of GM volume, WM volume, cortical thickness or white-matter tract integrity were reported in 72%, 50%, 56% and 33% of published studies respectively. Spatial distribution and direction of the disclosed effects were highly inconsistent across studies. The SDM-PSI analysis revealed neither significant decrease nor significant increase of GM volume, WM volume or cortical thickness in migraine. Overall there is to this day no strong evidence of specific brain anatomical alterations reliably associated to migraine. Possible explanations of this conflicting literature are discussed. Trial registration number: NCT02791997, registrated February 6th, 2015.

Is there a reliable brain morphological signature for migraine?

Voxel-based morphometry (VBM) is a popular non-invasive magnetic resonance imaging technique to investigate brain gray matter (GM) differences between groups. Recently, two VBM studies in migraine have been published in The Journal of Headache and Pain. Reviewing the two and those previous published VBM studies, we found considerable variations of the results. Spatially diverse brain regions with decreased and increased GM alterations and null findings have been reported. It is interesting to know whether there is a reliable brain morphological signature for migraine. Coordinate-based meta-analysis (CBMA) is increasingly used to quantitatively pool individual neuroimaging studies to identify consistent and reliable findings. Several CBMA have been conducted, however, their results were inconsistent. The algorithms for CBMA have evolved and more eligible VBM studies in migraine have been published. We therefore conducted an updated CBMA using the latest algorithms for CBMA, seed-based d mapping with permutation of subject images (SDM-PSI). The present CBMA of 32 VBM studies (41 datasets comprising 1252 patients and 1025 healthy controls) found no evidence of consistent GM alterations in migraine. Sensitivity analysis, subgroup meta-analyses, and meta-regression analyses revealed that the result was robust. This negative result indicates that there is no reliable brain morphological signature for migraine. VBM investigations in migraine remain a heterogeneous field. Many potential confounding factors, such as underpowered sample sizes, variations in demographic and clinical characteristics, and differences in MRI scanners, head coils, scanning parameters, preprocessing procedures, and statistical strategies may cause the inconsistences of the results. Future VBM studies are warranted to enroll well-characterized and homogeneous subtype samples with appropriate sample sizes, comprehensively assess comorbidities and medication status, and use well-validated and standardized imaging protocols and processing and analysis pipelines to produce robust and replicable results in migraine.

Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM.

Coordinate-based meta-analyses (CBMA) are very useful for summarizing the large number of voxel-based neuroimaging studies of normal brain functions and brain abnormalities in neuropsychiatric disorders. However, current CBMA methods do not conduct common voxelwise tests, but rather a test of convergence, which relies on some spatial assumptions that data may seldom meet, and has lower statistical power when there are multiple effects. Here we present a new algorithm that can use standard voxelwise tests and, importantly, conducts a standard permutation of subject images (PSI). Its main steps are: a) multiple imputation of study images; b) imputation of subject images; and c) subject-based permutation test to control the familywise error rate (FWER). The PSI algorithm is general and we believe that developers might implement it for several CBMA methods. We present here an implementation of PSI for seed-based d mapping (SDM) method, which additionally benefits from the use of effect sizes, random-effects models, Freedman-Lane-based permutations and threshold-free cluster enhancement (TFCE) statistics, among others. Finally, we also provide an empirical validation of the control of the FWER in SDM-PSI, which showed that it might be too conservative. We hope that the neuroimaging meta-analytic community will welcome this new algorithm and method.

Brain-based ranking of cognitive domains to predict schizophrenia.

Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalog of cognitive functions, we developed a data-driven machine-learning strategy and provide a proof of principle in a multisite clinical dataset (n = 324). Existing neuroscientific knowledge on diverse cognitive domains was first condensed into neurotopographical maps. We then examined how the ensuing meta-analytic cognitive priors can distinguish patients and controls using brain morphology and intrinsic functional connectivity. Some affected cognitive domains supported well-studied directions of research on auditory evaluation and social cognition. However, rarely suspected cognitive domains also emerged as disease relevant, including self-oriented processing of bodily sensations in gustation and pain. Such algorithmic charting of the cognitive landscape can be used to make targeted recommendations for future mental health research.

No reliable gray matter changes in essential tremor.

BACKGROUND: Voxel-based morphometry (VBM) has been used to study human brain gray matter (GM) alterations in essential tremor (ET) for over one decade. However, the literature revealed heterogeneous findings. METHODS: We therefore conducted a coordinate-based meta-analysis to synthesize the VBM studies to examine which brain regions show the most reliable GM alterations in patients with ET relative to healthy controls. RESULTS: A total of 16 original VBM studies, comprising 387 patients with ET and 355 healthy controls, were included in this meta-analysis. This quantitative meta-analysis revealed no evidence of robust and reliable alterations in regional brain GM structures in ET. Meta-regression analyses indicate that many moderators (e.g., MR field strength, statistical methodology, age, onset age, gender, illness severity, illness duration, and family history) account for some of the heterogeneity in GM across studies. CONCLUSIONS: High heterogeneity in GM alterations across studies may reflect true heterogeneity in ET regarding the clinic, etiology, and pathology, as well as possibly the VBM methodological variations. Currently, this heterogeneity limits the use of VBM as a reliable tool to distinguish ET from healthy controls. In order to improve reproducibility of VBM results in ET, future research may benefit from increasing the sample size, comprehensively subtyping ET phenotypes, and using well-designed and standardized imaging acquisition and analytical protocols. Furthermore, data sharing should be considered as a high priority.

What do results from coordinate-based meta-analyses tell us?

Coordinate-based meta-analyses (CBMA) methods, such as Activation Likelihood Estimation (ALE) and Seed-based d Mapping (SDM), have become an invaluable tool for summarizing the findings of voxel-based neuroimaging studies. However, the progressive sophistication of these methods may have concealed two particularities of their statistical tests. Common univariate voxelwise tests (such as the t/z-tests used in SPM and FSL) detect voxels that activate, or voxels that show differences between groups. Conversely, the tests conducted in CBMA test for "spatial convergence" of findings, i.e., they detect regions where studies report "more peaks than in most regions", regions that activate "more than most regions do", or regions that show "larger differences between groups than most regions do". The first particularity is that these tests rely on two spatial assumptions (voxels are independent and have the same probability to have a "false" peak), whose violation may make their results either conservative or liberal, though fortunately current versions of ALE, SDM and some other methods consider these assumptions. The second particularity is that the use of these tests involves an important paradox: the statistical power to detect a given effect is higher if there are no other effects in the brain, whereas lower in presence of multiple effects.

Toward a cumulative science of functional integration: A meta-analysis of psychophysiological interactions.

Much of the work in cognitive neuroscience is shifting from a focus on single brain regions to a focus on the connectivity between multiple brain regions. These inter-regional connectivity patterns contribute to a wide range of behaviors and are studied with models of functional integration. The rapid expansion of the literature on functional integration offers an opportunity to scrutinize the consistency and specificity of one of the most popular approaches for quantifying connectivity: psychophysiological interaction (PPI) analysis. We performed coordinate-based meta-analyses on 284 PPI studies, which allowed us to test (a) whether those studies consistently converge on similar target regions and (b) whether the identified target regions are specific to the chosen seed region and psychological context. Our analyses revealed two key results. First, we found that different types of PPI studies-e.g., those using seeds such as amygdala and dorsolateral prefrontal cortex (DLPFC) and contexts such as emotion and cognitive control, respectively-each consistently converge on similar target regions, thus supporting the reliability of PPI as a tool for studying functional integration. Second, we also found target regions that were specific to the chosen seed region and psychological context, indicating distinct patterns of brain connectivity. For example, the DLPFC seed reliably contributed to a posterior cingulate cortex target during cognitive control but contributed to an amygdala target in other contexts. Our results point to the robustness of PPI while highlighting common and distinct patterns of functional integration, potentially advancing models of brain connectivity. Hum Brain Mapp 37:2904-2917, 2016. © 2016 Wiley Periodicals, Inc.

Co-activation Probability Estimation (CoPE): An approach for modeling functional co-activation architecture based on neuroimaging coordinates.

Recent progress in functional neuroimaging has prompted studies of brain activation during various cognitive tasks. Coordinate-based meta-analysis has been utilized to discover the brain regions that are consistently activated across experiments. However, within-experiment co-activation relationships, which can reflect the underlying functional relationships between different brain regions, have not been widely studied. In particular, voxel-wise co-activation, which may be able to provide a detailed configuration of the co-activation network, still needs to be modeled. To estimate the voxel-wise co-activation pattern and deduce the co-activation network, a Co-activation Probability Estimation (CoPE) method was proposed to model within-experiment activations for the purpose of defining the co-activations. A permutation test was adopted as a significance test. Moreover, the co-activations were automatically separated into local and long-range ones, based on distance. The two types of co-activations describe distinct features: the first reflects convergent activations; the second represents co-activations between different brain regions. The validation of CoPE was based on five simulation tests and one real dataset derived from studies of working memory. Both the simulated and the real data demonstrated that CoPE was not only able to find local convergence but also significant long-range co-activation. In particular, CoPE was able to identify a 'core' co-activation network in the working memory dataset. As a data-driven method, the CoPE method can be used to mine underlying co-activation relationships across experiments in future studies.

Meta-analytic connectivity modeling revisited: controlling for activation base rates.

Co-activation of distinct brain regions is a measure of functional interaction, or connectivity, between those regions. The co-activation pattern of a given region can be investigated using seed-based activation likelihood estimation meta-analysis of functional neuroimaging data stored in databases such as BrainMap. This method reveals inter-regional functional connectivity by determining brain regions that are consistently co-activated with a given region of interest (the "seed") across a broad range of experiments. In current implementations of this meta-analytic connectivity modeling (MACM), significant spatial convergence (i.e. consistent co-activation) is distinguished from noise by comparing it against an unbiased null-distribution of random spatial associations between experiments according to which all gray-matter voxels have the same chance of convergence. As the a priori probability of finding activation in different voxels markedly differs across the brain, computing such a quasi-rectangular null-distribution renders the detection of significant convergence more likely in those voxels that are frequently activated. Here, we propose and test a modified MACM approach that takes this activation frequency bias into account. In this new specific co-activation likelihood estimation (SCALE) algorithm, a null-distribution is generated that reflects the base rate of reporting activation in any given voxel and thus equalizes the a priori chance of finding across-study convergence in each voxel of the brain. Using four exemplary seed regions (right visual area V4, left anterior insula, right intraparietal sulcus, and subgenual cingulum), our tests corroborated the enhanced specificity of the modified algorithm, indicating that SCALE may be especially useful for delineating distinct core networks of co-activation.

A novel meta-analytic approach: mining frequent co-activation patterns in neuroimaging databases.

In recent years, coordinate-based meta-analyses have become a powerful and widely used tool to study co-activity across neuroimaging experiments, a development that was supported by the emergence of large-scale neuroimaging databases like BrainMap. However, the evaluation of co-activation patterns is constrained by the fact that previous coordinate-based meta-analysis techniques like Activation Likelihood Estimation (ALE) and Multilevel Kernel Density Analysis (MKDA) reveal all brain regions that show convergent activity within a dataset without taking into account actual within-experiment co-occurrence patterns. To overcome this issue we here propose a novel meta-analytic approach named PaMiNI that utilizes a combination of two well-established data-mining techniques, Gaussian mixture modeling and the Apriori algorithm. By this, PaMiNI enables a data-driven detection of frequent co-activation patterns within neuroimaging datasets. The feasibility of the method is demonstrated by means of several analyses on simulated data as well as a real application. The analyses of the simulated data show that PaMiNI identifies the brain regions underlying the simulated activation foci and perfectly separates the co-activation patterns of the experiments in the simulations. Furthermore, PaMiNI still yields good results when activation foci of distinct brain regions become closer together or if they are non-Gaussian distributed. For the further evaluation, a real dataset on working memory experiments is used, which was previously examined in an ALE meta-analysis and hence allows a cross-validation of both methods. In this latter analysis, PaMiNI revealed a fronto-parietal "core" network of working memory and furthermore indicates a left-lateralization in this network. Finally, to encourage a widespread usage of this new method, the PaMiNI approach was implemented into a publicly available software system.

Activation Likelihood Estimation Neuroimaging Meta-Analysis: a Powerful Tool for Emotion Research.

Over the past two decades, functional magnetic resonance imaging (fMRI) has become the primary tool for exploring neural correlates of emotion. To enhance the reliability of results in understanding the complex nature of emotional experiences, researchers combine findings from multiple fMRI studies using coordinate-based meta-analysis (CBMA). As one of the most widely employed CBMA methods worldwide, activation likelihood estimation (ALE) is of great importance in affective neuroscience and neuropsychology. This comprehensive review provides an introductory guide for implementing the ALE method in emotion research, outlining the experimental steps involved. By presenting a case study about the emotion of disgust, with regard to both its core and social processing, we offer insightful commentary as to how ALE can enable researchers to produce consistent results and, consequently, fruitfully investigate the neural mechanisms underpinning emotions, facilitating further progress in this field.

Introduction to the Meta-analysis method of functional magnetic resonance imaging research and the exploration of its application in the field of acupuncture. / åŠŸèƒ½ç£å ±æŒ¯ç ”ç©¶å ƒåˆ†æžæ–¹æ³•ä»‹ç»åŠå ¶åœ¨é’ˆåˆºé¢†åŸŸçš„åº”ç”¨æŽ¢æž.

In recent years, the number of functional magnetic resonance imaging (fMRI) research in acupuncture grows increasingly. However, due to the differences in acupoint selection, acupuncture technique and sample size, the problems get more prominent in terms of the diverse results and the lack of common rules of acupuncture among researches. By taking the fMRI research for post-stroke motor dysfunction (PSMD) treated with acupuncture as the example, this paper introduces the fMRI Meta-analysis technology for integrating the relevant research results and extracting the common rules, namely image-based Meta-analysis (IBMA) and coordinate-based Meta-analysis (CBMA). Considering the higher feasibility of CBMA, three available CBMA methods are explained specially, including activation likelihood estimation (ALE), kernel density analysis (KDA), and seed-based d mapping (SDM). Focusing on the precautions and operation procedure of CBMA, the review is conducted systematically on the type of fMRI research, task design, analytical method, and the thinking integrity of fMRI Meta-analysis, and the review findings are collated in charts. It aims to assist readers to understand the abstract and complex theories and practical information of this technology efficiently, conveniently and systematically, and hopes to provide the references for the future learning and the application.

Neural alterations underlying executive dysfunction in Parkinson's disease: A systematic review and coordinate-based meta-analysis of functional neuroimaging studies.

Parkinson's Disease's (PD) neuropsychological profile is often characterized by altered performance in executive functions (EF) tasks, with a remarkable impact on patients' quality of life. To date, the available neuroimaging literature lacks conclusive evidence about neural patterns underlying EF deficits in PD. Here, we aimed to synthesize the results of PET/fMRI studies examining the differences in brain activation between PD patients and controls during EF tasks, focusing on the three main EF sub-components: cognitive flexibility, working memory, and response inhibition. We conducted a coordinate-based meta-analysis to assess the converging alterations in brain activity in PD patients compared to controls. We assessed the association between aberrant patterns of activity and the EF sub-domains. We found a significant association between hypoactivation patterns in PD converging at the level of the right inferior frontal gyrus in response inhibition tasks, whereas hypoactivation in the left inferior frontal gyrus was found in association with the cognitive flexibility domain. Our results confirm the existence of neural alterations in PD patients in relation to specific EF sub-domains.

Similarities and differences between post-traumatic stress disorder and major depressive disorder: Evidence from task-evoked functional magnetic resonance imaging meta-analysis.

BACKGROUND: Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) are psychiatric disorders that can present with overlapping symptoms and shared risk factors. However, the extent to which these disorders share common underlying neuropathological mechanisms remains unclear. To investigate the similarities and differences in task-evoked brain activation patterns between patients with PTSD and MDD. METHODS: A coordinate-based meta-analysis was conducted across 35 PTSD studies (564 patients and 543 healthy controls) and 125 MDD studies (4049 patients and 4170 healthy controls) using anisotropic effect-size signed differential mapping software. RESULTS: Both PTSD and MDD patients exhibited increased neural activation in the bilateral inferior frontal gyrus. However, PTSD patients showed increased neural activation in the right insula, left supplementary motor area extending to median cingulate gyrus and superior frontal gyrus (SFG), and left fusiform gyrus, and decreased neural activation in the right posterior cingulate gyrus, right middle temporal gyrus, right paracentral lobule, and right inferior parietal gyrus relative to MDD patients. CONCLUSION: Our meta-analysis suggests that PTSD and MDD share some similar patterns of brain activation, but also have distinct neural signatures. These findings contribute to our understanding of the potential neuropathology underlying these disorders and may inform the development of more targeted and effective treatment and intervention strategies. Moreover, these results may provide useful neuroimaging targets for the differential diagnosis of MDD and PTSD.