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.

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.

Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate.

A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative results revealed shared and human-specific RSFC of different striatal clusters reinforcing the complex organization and function of the striatum. In addition, we provided a testable hypothesis that abnormalities in a region with human-specific connectivity, i.e., dorsal caudate, might be associated with neuropsychiatric disorders.

ENIGMA-Sleep: Challenges, opportunities, and the road map.

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.

Gray matter volume and estimated brain age gap are not linked with sleep-disordered breathing.

Alzheimer's disease (AD) and sleep-disordered breathing (SDB) are prevalent conditions with a rising burden. It is suggested that SDB may contribute to cognitive decline and advanced aging. Here, we assessed the link between self-reported SDB and gray matter volume in patients with AD, mild cognitive impairment (MCI) and healthy controls (HCs). We further investigated whether SDB was associated with advanced brain aging. We included a total of 330 participants, divided based on self-reported history of SDB, and matched across diagnoses for age, sex and presence of the Apolipoprotein E4 allele, from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Gray-matter volume was measured using voxel-wise morphometry and group differences in terms of SDB, cognitive status, and their interaction were assessed. Further, using an age-prediction model fitted on gray-matter data of external datasets, we predicted study participants' age from their structural images. Cognitive decline and advanced age were associated with lower gray matter volume in various regions, particularly in the bilateral temporal lobes. Brains age was well predicted from the morphological data in HCs and, as expected, elevated in MCI and particularly in AD subjects. However, there was neither a significant difference between regional gray matter volume in any diagnostic group related to the SDB status, nor in SDB-by-cognitive status interaction. Moreover, we found no difference in estimated chronological age gap related to SDB, or by-cognitive status interaction. Contrary to our hypothesis, we were not able to find a general or a diagnostic-dependent association of SDB with either gray-matter volumetric or brain aging.

Functional Connectivity Changes of Key Regions for Motor Initiation in Parkinson's Disease.

Akinesia, a cardinal symptom of Parkinson's disease, has been linked to abnormal activation in putamen and posterior medial frontal cortex (pMFC). However, little is known whether clinical severity of akinesia is linked to dysfunctional connectivity of these regions. Using a seed-based approach, we here investigated resting-state functional connectivity (RSFC) of putamen, pMFC and primary motor cortex (M1) in 60 patients with Parkinson's disease on regular medication and 72 healthy controls. We found that in patients putamen featured decreases of connectivity for a number of cortical and subcortical areas engaged in sensorimotor and cognitive processing. In contrast, the pMFC showed reduced connectivity with a more focal cortical network involved in higher-level motor-cognition. Finally, M1 featured a selective disruption of connectivity in a network specifically connected with M1. Correlating clinical impairment with connectivity changes revealed a relationship between akinesia and reduced RSFC between pMFC and left intraparietal lobule (IPL). Together, the present study demonstrated RSFC decreases in networks for motor initiation and execution in Parkinson's disease. Moreover, results suggest a relationship between pMFC-IPL decoupling and the manifestation of akinetic symptoms.

Practical recommendations to conduct a neuroimaging meta-analysis for neuropsychiatric disorders.

Over the past decades, neuroimaging has become widely used to investigate structural and functional brain abnormality in neuropsychiatric disorders. The results of individual neuroimaging studies, however, are frequently inconsistent due to small and heterogeneous samples, analytical flexibility, and publication bias toward positive findings. To consolidate the emergent findings toward clinically useful insight, meta-analyses have been developed to integrate the results of studies and identify areas that are consistently involved in pathophysiology of particular neuropsychiatric disorders. However, it should be considered that the results of meta-analyses could also be divergent due to heterogeneity in search strategy, selection criteria, imaging modalities, behavioral tasks, number of experiments, data organization methods, and statistical analysis with different multiple comparison thresholds. Following an introduction to the problem and the concepts of quantitative summaries of neuroimaging findings, we propose practical recommendations for clinicians and researchers for conducting transparent and methodologically sound neuroimaging meta-analyses. This should help to consolidate the search for convergent regional brain abnormality in neuropsychiatric disorders.

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.

Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation.

Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis.

ANIMA: A data-sharing initiative for neuroimaging meta-analyses.

Meta-analytic techniques allow cognitive neuroscientists to pool large amounts of data across many individual task-based functional neuroimaging experiments. These methods have been aided by the introduction of online databases such as Brainmap.org or Neurosynth.org, which collate peak activation coordinates obtained from thousands of published studies. Findings from meta-analytic studies typically include brain regions which are consistently activated across studies for specific contrasts, investigating cognitive or clinical hypotheses. These regions can be subsequently used as the basis for seed-based connectivity analysis, or formally compared to neuroimaging data in order to help interpret new findings. To facilitate such approaches, we have developed a new online repository of meta-analytic neuroimaging results, named the Archive of Neuroimaging Meta-analyses (ANIMA). The ANIMA platform consists of an intuitive online interface for querying, downloading, and contributing data from published meta-analytic studies. Additionally, to aid the process of organizing, visualizing, and working with these data, we present an open-source desktop application called Volume Viewer. Volume Viewer allows users to easily arrange imaging data into composite stacks, and save these sessions as individual files, which can also be uploaded to the ANIMA database. The application also allows users to perform basic functions, such as computing conjunctions between images, or extracting regions-of-interest or peak coordinates for further analysis. The introduction of this new resource will enhance the ability of researchers to both share their findings and incorporate existing meta-analytic results into their own research.

Functional Connectivity Differences of the Subthalamic Nucleus Related to Parkinson's Disease.

A typical feature of Parkinson's disease (PD) is pathological activity in the subthalamic nucleus (STN). Here, we tested whether in patients with PD under dopaminergic treatment functional connectivity of the STN differs from healthy controls (HC) and whether some brain regions show (anti-) correlations between functional connectivity with STN and motor symptoms. We used functional magnetic resonance imaging to investigate whole-brain resting-state functional connectivity with STN in 54 patients with PD and 55 HC matched for age, gender, and within-scanner motion. Compared to HC, we found attenuated negative STN-coupling with Crus I of the right cerebellum and with right ventromedial prefrontal regions in patients with PD. Furthermore, we observed enhanced negative STN-coupling with bilateral intraparietal sulcus/superior parietal cortex, right sensorimotor, right premotor, and left visual cortex compared to HC. Finally, we found a decline in positive STN-coupling with the left insula related to severity of motor symptoms and a decline of inter-hemispheric functional connectivity between left and right STN with progression of PD-related motor symptoms. Motor symptom related uncoupling of the insula, a key region in the saliency network and for executive function, from the STN might be associated with well-known executive dysfunction in PD. Moreover, uncoupling between insula and STN might also induce an insufficient setting of thresholds for the discrimination between relevant and irrelevant salient environmental stimuli, explaining observations of disturbed response control in PD. In sum, motor symptoms in PD are associated with a reduced coupling between STN and a key region for executive function.

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.

Multimodal connectivity of motor learning-related dorsal premotor cortex.

The dorsal premotor cortex (dPMC) is a key region for motor learning and sensorimotor integration, yet we have limited understanding of its functional interactions with other regions. Previous work has started to examine functional connectivity in several brain areas using resting state functional connectivity (RSFC) and meta-analytical connectivity modelling (MACM). More recently, structural covariance (SC) has been proposed as a technique that may also allow delineation of functional connectivity. Here, we applied these three approaches to provide a comprehensive characterization of functional connectivity with a seed in the left dPMC that a previous meta-analysis of functional neuroimaging studies has identified as playing a key role in motor learning. Using data from two sources (the Rockland sample, containing resting state data and anatomical scans from 132 participants, and the BrainMap database, which contains peak activation foci from over 10,000 experiments), we conducted independent whole-brain functional connectivity mapping analyses of a dPMC seed. RSFC and MACM revealed similar connectivity maps spanning prefrontal, premotor, and parietal regions, while the SC map identified more widespread frontal regions. Analyses indicated a relatively consistent pattern of functional connectivity between RSFC and MACM that was distinct from that identified by SC. Notably, results indicate that the seed is functionally connected to areas involved in visuomotor control and executive functions, suggesting that the dPMC acts as an interface between motor control and cognition.

The intrinsic resting state voice network in Parkinson's disease.

Over 90 percent of patients with Parkinson's disease experience speech-motor impairment, namely, hypokinetic dysarthria characterized by reduced pitch and loudness. Resting-state functional connectivity analysis of blood oxygen level-dependent functional magnetic resonance imaging is a useful measure of intrinsic neural functioning. We utilized resting-state functional connectivity modeling to analyze the intrinsic connectivity in patients with Parkinson's disease within a vocalization network defined by a previous meta-analysis of speech (Brown et al., 2009). Functional connectivity of this network was assessed in 56 patients with Parkinson's disease and 56 gender-, age-, and movement-matched healthy controls. We also had item 5 and 18 of the UPDRS, and the PDQ-39 Communication subscale available for correlation with the voice network connectivity strength in patients. The within-group analyses of connectivity patterns demonstrated a lack of subcortical-cortical connectivity in patients with Parkinson's disease. At the cortical level, we found robust (homotopic) interhemispheric connectivity but only inconsistent evidence for many intrahemispheric connections. When directly contrasted to the control group, we found a significant reduction of connections between the left thalamus and putamen, and cortical motor areas, as well as reduced right superior temporal gyrus connectivity. Furthermore, most symptom measures correlated with right putamen, left cerebellum, left superior temporal gyrus, right premotor, and left Rolandic operculum connectivity in the voice network. The results reflect the importance of (right) subcortical nodes and the superior temporal gyrus in Parkinson's disease, enhancing our understanding of the neurobiological underpinnings of vocalization impairment in Parkinson's disease.

Structural imaging studies of patients with chronic pain: an anatomical likelihood estimate meta-analysis.

ABSTRACT: Neuroimaging is a powerful tool to investigate potential associations between chronic pain and brain structure. However, the proliferation of studies across diverse chronic pain syndromes and heterogeneous results challenges data integration and interpretation. We conducted a preregistered anatomical likelihood estimate meta-analysis on structural magnetic imaging studies comparing patients with chronic pain and healthy controls. Specifically, we investigated a broad range of measures of brain structure as well as specific alterations in gray matter and cortical thickness. A total of 7849 abstracts of experiments published between January 1, 1990, and April 26, 2021, were identified from 8 databases and evaluated by 2 independent reviewers. Overall, 103 experiments with a total of 5075 participants met the preregistered inclusion criteria. After correction for multiple comparisons using the gold-standard family-wise error correction ( P < 0.05), no significant differences associated with chronic pain were found. However, exploratory analyses using threshold-free cluster enhancement revealed several spatially distributed clusters showing structural alterations in chronic pain. Most of the clusters coincided with regions implicated in nociceptive processing including the amygdala, thalamus, hippocampus, insula, anterior cingulate cortex, and inferior frontal gyrus. Taken together, these results suggest that chronic pain is associated with subtle, spatially distributed alterations of brain structure.

Functional imaging studies of acute administration of classic psychedelics, ketamine, and MDMA: Methodological limitations and convergent results.

Functional magnetic resonance imaging (fMRI) is increasingly used to non-invasively study the acute impact of psychedelics on the human brain. While fMRI is a promising tool for measuring brain function in response to psychedelics, it also has known methodological challenges. We conducted a systematic review of fMRI studies examining acute responses to experimentally administered psychedelics in order to identify convergent findings and characterize heterogeneity in the literature. We reviewed 91 full-text papers; these studies were notable for substantial heterogeneity in design, task, dosage, drug timing, and statistical approach. Data recycling was common, with 51 unique samples across 91 studies. Fifty-seven studies (54%) did not meet contemporary standards for Type I error correction or control of motion artifact. Psilocybin and LSD were consistently reported to moderate the connectivity architecture of the sensorimotor-association cortical axis. Studies also consistently reported that ketamine administration increased activation in the dorsomedial prefrontal cortex. Moving forward, use of best practices such as pre-registration, standardized image processing and statistical testing, and data sharing will be important in this rapidly developing field.

Convergent regional brain abnormalities in behavioral variant frontotemporal dementia: A neuroimaging meta-analysis of 73 studies.

Introduction: Numerous studies have reported brain alterations in behavioral variant frontotemporal dementia (bvFTD). However, they pointed to inconsistent findings. Methods: We used a meta-analytic approach to identify the convergent structural and functional brain abnormalities in bvFTD. Following current best-practice neuroimaging meta-analysis guidelines, we searched PubMed and Embase databases and performed reference tracking. Then, the coordinates of group comparisons between bvFTD and controls from 73 studies were extracted and tested for convergence using activation likelihood estimation. Results: We identified convergent abnormalities in the anterior cingulate cortices, anterior insula, amygdala, paracingulate, striatum, and hippocampus. Task-based and resting-state functional connectivity pointed to the networks that are connected to the obtained consistent regions. Functional decoding analyses suggested associated dysfunction of emotional processing, interoception, reward processing, higher-order cognitive functions, and olfactory and gustatory perceptions in bvFTD. Discussion: Our findings highlighted the key role of the salience network and subcortical regions in the pathophysiology of bvFTD.

Advanced brain ageing in Parkinson's disease is related to disease duration and individual impairment.

Machine learning can reliably predict individual age from MRI data, revealing that patients with neurodegenerative disorders show an elevated biological age. A surprising gap in the literature, however, pertains to Parkinson's disease. Here, we evaluate brain age in two cohorts of Parkinson's patients and investigated the relationship between individual brain age and clinical characteristics. We assessed 372 patients with idiopathic Parkinson's disease, newly diagnosed cases from the Parkinson's Progression Marker Initiative database and a more chronic local sample, as well as age- and sex-matched healthy controls. Following morphometric preprocessing and atlas-based compression, individual brain age was predicted using a multivariate machine learning model trained on an independent, multi-site reference sample. Across cohorts, healthy controls were well predicted with a mean error of 4.4 years. In turn, Parkinson's patients showed a significant (controlling for age, gender and site) increase in brain age of ∼3 years. While this effect was already present in the newly diagnosed sample, advanced biological age was significantly related to disease duration as well as worse cognitive and motor impairment. While biological age is increased in patients with Parkinson's disease, the effect is at the lower end of what is found for other neurological and psychiatric disorders. We argue that this may reflect a heterochronicity between forebrain atrophy and small but behaviourally salient midbrain pathology. Finally, we point to the need to disentangle physiological ageing trajectories, lifestyle effects and core pathological changes.

Brain Responses to Noxious Stimuli in Patients With Chronic Pain: A Systematic Review and Meta-analysis.

Importance: Functional neuroimaging is a valuable tool for understanding how patients with chronic pain respond to painful stimuli. However, past studies have reported heterogenous results, highlighting opportunities for a quantitative meta-analysis to integrate existing data and delineate consistent associations across studies. Objective: To identify differential brain responses to noxious stimuli in patients with chronic pain using functional magnetic resonance imaging (fMRI) while adhering to current best practices for neuroimaging meta-analyses. Data Sources: All fMRI experiments published from January 1, 1990, to May 28, 2019, were identified in a literature search of PubMed/MEDLINE, EMBASE, Web of Science, Cochrane Library, PsycINFO, and SCOPUS. Study Selection: Experiments comparing brain responses to noxious stimuli in fMRI between patients and controls were selected if they reported whole-brain results, included at least 10 patients and 10 healthy control participants, and used adequate statistical thresholding (voxel-height P < .001 or cluster-corrected P < .05). Two independent reviewers evaluated titles and abstracts returned by the search. In total, 3682 abstracts were screened, and 1129 full-text articles were evaluated. Data Extraction and Synthesis: Thirty-seven experiments from 29 articles met inclusion criteria for meta-analysis. Coordinates reporting significant activation differences between patients with chronic pain and healthy controls were extracted. These data were meta-analyzed using activation likelihood estimation. Data were analyzed from December 2019 to February 2020. Main Outcomes and Measures: A whole-brain meta-analysis evaluated whether reported differences in brain activation in response to noxious stimuli between patients and healthy controls were spatially convergent. Follow-up analyses examined the directionality of any differences. Finally, an exploratory (nonpreregistered) region-of-interest analysis examined differences within the pain network. Results: The 37 experiments from 29 unique articles included a total of 511 patients and 433 controls (944 participants). Whole-brain meta-analyses did not reveal significant differences between patients and controls in brain responses to noxious stimuli at the preregistered statistical threshold. However, exploratory analyses restricted to the pain network revealed aberrant activity in patients. Conclusions and Relevance: In this systematic review and meta-analysis, preregistered, whole-brain analyses did not reveal aberrant fMRI activity in patients with chronic pain. Exploratory analyses suggested that subtle, spatially diffuse differences may exist within the pain network. Future work on chronic pain biomarkers may benefit from focus on this core set of pain-responsive areas.