Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies

Neuroimaging meta regression for coordinate based meta analysis data with a spatial model

Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. Keywords: generalized linear models; meta-analysis; spatial statistics; statistical modeling.

Domestic dogs as a comparative model for social neuroscience: Advances and challenges

Dogs and humans have lived together for thousands of years and share many analogous socio-cognitive skills. Dog neuroimaging now provides insight into the neural bases of these shared social abilities. Here, we summarize key findings from dog fMRI identifying neocortical brain areas implicated in visual social cognition, such as face, body, and emotion perception, as well as action observation in dogs. These findings provide converging evidence that the temporal cortex plays a significant role in visual social cognition in dogs. We further briefly review investigations into the neural base of the dog-human relationship, mainly involving limbic brain regions. We then discuss current challenges in the field, such as statistical power and lack of common template spaces, and how to overcome them. Finally, we argue that the foundation has now been built to investigate and compare the neural bases of more complex socio-cognitive phenomena shared by dogs and humans. This will strengthen and expand the role of the domestic dog as a powerful comparative model species and provide novel insights into the evolutionary roots of social cognition.

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.

Grey matter alterations in generalized anxiety disorder: A voxel-wise meta-analysis of voxel-based morphometry studies

OBJECTIVE

Grey matter, a crucial component of the brain, has been found altered in generalized anxiety disorder (GAD) of several voxel-based morphometry studies. The conclusive and consistent grey matter alterations in GAD have not been confirmed.

METHOD

Eleven voxel-based morphometry studies of GAD patients were included in the current systematic review and meta-analysis. The linear model of anxiety severity scores was applied to explore the relationship of grey matter alterations and anxiety severity. The subgroup analysis of adult GAD and adolescent GAD was also performed.

RESULTS

Significantly modest grey matter alterations in the left superior temporal gyrus of patients with GAD were found. The anxiety severity score was significantly correlated with grey matter alterations in the right insula, lenticular nucleus, putamen and striatum. The subgroup analysis of adult GAD and adolescent GAD all failed to show significant grey matter alterations. However, in the adult GAD subgroup, anxiety severity score was significantly correlated with grey matter alterations in the right insula.

CONCLUSION

GAD might have the modest grey matter alterations in the left superior temporal gyrus. Anxiety severity might be related to the grey matter alterations in the limbic regions, such as the right insula, lenticular nucleus, putamen and striatum. This kind of correlation might be related to the effects of adult GAD. Future studies with adequate sample sizes and sophisticated GAD categories will be needed.

Large-scale network abnormality in behavioral addiction

BACKGROUND

Researchers have endeavored to ascertain the network dysfunction associated with behavioral addiction (BA) through the utilization of resting-state functional connectivity (rsFC). Nevertheless, the identification of aberrant patterns within large-scale networks pertaining to BA has proven to be challenging.

METHODS

Whole-brain seed-based rsFC studies comparing subjects with BA and healthy controls (HC) were collected from multiple databases. Multilevel kernel density analysis was employed to ascertain brain networks in which BA was linked to hyper-connectivity or hypo-connectivity with each prior network.

RESULTS

Fifty-six seed-based rsFC publications (1755 individuals with BA and 1828 HC) were included in the meta-analysis. The present study indicate that individuals with BAs exhibit (1) hypo-connectivity within the fronto-parietal network (FN) and hypo- and hyper-connectivity within the ventral attention network (VAN); (2) hypo-connectivity between the FN and regions of the VAN, hypo-connectivity between the VAN and regions of the FN and default mode network (DMN), hyper-connectivity between the DMN and regions of the FN; (3) hypo-connectivity between the reward system and regions of the sensorimotor network (SS), DMN and VAN; (4) hypo-connectivity between the FN and regions of the SS, hyper-connectivity between the VAN and regions of the SS.

CONCLUSIONS

These findings provide impetus for a conceptual framework positing a model of BA characterized by disconnected functional coordination among large-scale networks.

Grey matter structural alterations in anxiety disorders: a voxel-based meta-analysis

Anxiety disorders (ADs) are a group of prevalent and destructive mental illnesses, but the current understanding of their underlying neuropathology is still unclear. Employing voxel-based morphometry (VBM), previous studies have demonstrated several common brain regions showing grey matter volume (GMV) abnormalities. However, contradictory results have been reported among these studies. Considering that different subtypes of ADs exhibit common core symptoms despite different diagnostic criteria, and previous meta-analyses have found common core GMV-altered brain regions in ADs, the present research aimed to combine the results of individual studies to identify common GMV abnormalities in ADs. Therefore, we first performed a systematic search in PubMed, Embase, and Web of Science on studies investigating GMV differences between patients with ADs and healthy controls (HCs). Then, the anisotropic effect-size signed differential mapping (AES-SDM) was applied in this meta-analysis. A total of 24 studies (including 25 data sets) were included in the current study, and 906 patients with ADs and 1003 HCs were included. Compared with the HCs, the patients with ADs showed increased GMV in the left superior parietal gyrus, right angular gyrus, left precentral gyrus, and right lingual gyrus, and decreased GMV in the bilateral insula, bilateral thalamus, left caudate, and right putamen. In conclusion, the current study has identified some abnormal GMV brain regions that are related to the pathological mechanisms of anxiety disorders. These findings could contribute to a better understanding of the underlying neuropathology of ADs.

Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression

It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is present in patients with schizophrenia, yet there are inconsistencies in the relevant findings. Moreover, little is known about their association with brain gene expression profiles. In this study, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control VMHC differences from both the discovery (meta-analysis, including 9 studies with a total of 386 patients and 357 controls) and replication (separate group-level comparisons within two datasets, including a total of 258 patients and 287 controls) phases were performed to identify genes associated with VMHC alterations. Enrichment analyses were conducted to characterize the biological functions and specific expression of identified genes, and Neurosynth decoding analysis was performed to examine the correlation between cognitive-related processes and VMHC alterations in schizophrenia. In the discovery and replication phases, patients with schizophrenia exhibited consistent VMHC changes compared to controls, which were correlated with a series of cognitive-related processes; meta-regression analysis revealed that illness duration was negatively correlated with VMHC abnormalities in the cerebellum and postcentral/precentral gyrus. The abnormal VMHC patterns were stably correlated with 1287 genes enriched for fundamental biological processes like regulation of cell communication, nervous system development, and cell communication. In addition, these genes were overexpressed in astrocytes and immune cells, enriched in extensive cortical regions and wide developmental time windows. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying VMHC alterations in patients with schizophrenia.

Blunted brain responses to neutral faces in healthy first-degree relatives of patients with schizophrenia: an image-based fMRI meta-analysis

Schizophrenia is characterized by the misattribution of emotional significance to neutral faces, accompanied by overactivations of the limbic system. To understand the disorder's genetic and environmental contributors, investigating healthy first-degree relatives is crucial. However, inconsistent findings exist regarding their ability to recognize neutral faces, with limited research exploring the cerebral correlates of neutral face processing in this population. Thus, we here investigated brain responses to neutral face processing in healthy first-degree relatives through an image-based meta-analysis of functional magnetic resonance imaging studies. We included unthresholded group-level T-maps from 5 studies comprising a total of 120 first-degree relatives and 150 healthy controls. In sensitivity analyses, we ran a combined image- and coordinate-based meta-analysis including 7 studies (157 first-degree relatives, 207 healthy controls) aiming at testing the robustness of the results in a larger sample of studies. Our findings revealed a pattern of decreased brain responses to neutral faces in relatives compared with healthy controls, particularly in limbic areas such as the bilateral amygdala, hippocampus, and insula. The same pattern was observed in sensitivity analyses. These results contrast with the overactivations observed in patients, potentially suggesting that this trait could serve as a protective factor in healthy relatives. However, further research is necessary to test this hypothesis.

A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework

Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns of the RDoC framework, our study employed a latent variable approach, specifically utilizing bifactor analysis. We examined a total of 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with a total of 6,192 participants. Within this set of 84 maps, a curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set of our analysis, and the remaining held-out maps formed the internal validation set. External validation was performed with 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Our results indicate that a bifactor model with a task-general domain and splitting the cognitive systems domain into sub-domains better fits the current corpus of tfMRI data than the current RDoC framework. Our data-driven validation supports revising the RDoC framework to accurately reflect underlying brain circuitry.

Influence of transient emotional episodes on affective and cognitive theory of mind

Our emotions may influence how we interact with others. Previous studies have shown an important role of emotion induction in generating empathic reactions towards others' affect. However, it remains unclear whether (and to which extent) our own emotions can influence the ability to infer people's mental states, a process associated with Theory of Mind (ToM) and implicated in the representation of both cognitive (e.g. beliefs and intentions) and affective conditions. We engaged 59 participants in two emotion-induction experiments where they saw joyful, neutral and fearful clips. Subsequently, they were asked to infer other individuals' joy, fear (affective ToM) or beliefs (cognitive ToM) from verbal scenarios. Using functional magnetic resonance imaging, we found that brain activity in the superior temporal gyrus, precuneus and sensorimotor cortices were modulated by the preceding emotional induction, with lower response when the to-be-inferred emotion was incongruent with the one induced in the observer (affective ToM). Instead, we found no effect of emotion induction on the appraisal of people's beliefs (cognitive ToM). These findings are consistent with embodied accounts of affective ToM, whereby our own emotions alter the engagement of key brain regions for social cognition, depending on the compatibility between one's own and others' affect.

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.

Functional Connectivity of the Nucleus Accumbens across Variants of Callous-Unemotional Traits: A Resting-State fMRI Study in Children and Adolescents

A large body of literature suggests that the primary (high callousness-unemotional traits [CU] and low anxiety) and secondary (high CU traits and anxiety) variants of psychopathy significantly differ in terms of their clinical profiles. However, little is known about their neurobiological differences. While few studies showed that variants differ in brain activity during fear processing, it remains unknown whether they also show atypical functioning in motivational and reward system. Latent Profile Analysis (LPA) was conducted on a large sample of adolescents (n = 1416) to identify variants based on their levels of callousness and anxiety. Seed-to-voxel connectivity analysis was subsequently performed on resting-state fMRI data to compare connectivity patterns of the nucleus accumbens across subgroups. LPA failed to identify the primary variant when using total score of CU traits. Using a family-wise cluster correction, groups did not differ on functional connectivity. However, at an uncorrected threshold the secondary variant showed distinct functional connectivity between the nucleus accumbens and posterior insula, lateral orbitofrontal cortex, supplementary motor area, and parietal regions. Secondary LPA analysis using only the callousness subscale successfully distinguish both variants. Group differences replicated results of deficits in functional connectivity between the nucleus accumbens and posterior insula and supplementary motor area, but additionally showed effect in the superior temporal gyrus which was specific to the primary variant. The current study supports the importance of examining the neurobiological markers across subgroups of adolescents at risk for conduct problems to precise our understanding of this heterogeneous population.

Neural Correlates of Semantic Interference and Phonological Facilitation in Picture Naming: A Systematic Review and Coordinate-Based Meta-analysis

Semantic interference (SI) and phonological facilitation (PF) effects occur when multiple representations are co-activated simultaneously in complex naming paradigms, manipulating the context in which word production is set. Although the behavioral consequences of these psycholinguistic effects are well-known, the involved brain structures are still controversial. This paper aims to provide a systematic review and a coordinate-based meta-analysis of the available functional neuroimaging studies investigating SI and PF in picture naming paradigms. The included studies were fMRI experiments on healthy subjects, employing paradigms in which co-activations of representations were obtained by manipulating the naming context using semantically or phonologically related items. We examined the principal methodological aspects of the included studies, emphasizing the existing commonalities and discrepancies across single investigations. We then performed an exploratory coordinate-based meta-analysis of the reported activation peaks of neural response related to SI and PF. Our results consolidated previous findings regarding the involvement of the left inferior frontal gyrus and the left middle temporal gyrus in SI and brought out the role of bilateral inferior parietal regions in PF.

Novel hippocampal genes involved in enhanced susceptibility to chronic pain-induced behavioral emotionality

Altered mood and psychiatric disorders are commonly associated with chronic pain conditions; however, brain mechanisms linking pain and comorbid clinical depression are still largely unknown. In this study, we aimed to identify whether key genes/cellular mechanisms underlie susceptibility/resiliency to development of depressive-like behaviors during chronic pain state. Genome-wide RNA-seq analysis was used to examine the transcriptomic profile of the hippocampus, a limbic brain region that regulates mood and stress responses, from male rats exposed to chronic inflammatory pain. Pain-exposed animals were separated into either 'resilient' or 'susceptible' to development of enhanced behavioral emotionality based on behavioral testing. RNA-seq bioinformatic analysis, followed by validation using qPCR, revealed dysregulation of hippocampal genes involved in neuroinflammation, cell cycle/neurogenesis and blood-brain barrier integrity. Specifically, ADAM Metallopeptidase Domain 8 (Adam8) and Aurora Kinase B (Aurkb), genes with functional roles in activation of the NLRP3 inflammasome and microgliosis, respectively, were significantly upregulated in the hippocampus of 'susceptible' animals expressing increased behavioral emotionality. In addition, genes associated with blood-brain barrier integrity, such as the Claudin 4 (Cldn4), a tight junction protein and a known marker of astrocyte activation, were also significantly dysregulated between 'resilient' or 'susceptible' pain groups. Furthermore, differentially expressed genes (DEGs) were further characterized in rodents stress models to determine whether their hippocampal dysregulation is driven by common stress responses vs. affective pain processing. Altogether these results continue to strengthen the connection between dysregulation of hippocampal genes involved in neuroinflammatory and neurodegenerative processes with increased behavioral emotionality often expressed in chronic pain state.

Similarity and difference in large-scale functional network alternations between behavioral addictions and substance use disorder: a comparative meta-analysis

Behavioral addiction (BA) and substance use disorder (SUD) share similarities and differences in clinical symptoms, cognitive functions, and behavioral attributes. However, little is known about whether and how functional networks in the human brain manifest commonalities and differences between BA and SUD. Voxel-wise meta-analyses of resting-state functional connectivity (rs-FC) were conducted in BA and SUD separately, followed by quantitative conjunction analyses to identify the common and distinct alterations across both the BA and SUD groups. A total of 92 datasets with 2444 addicted patients and 2712 healthy controls (HCs) were eligible for the meta-analysis. Our findings demonstrated that BA and SUD exhibited common alterations in rs-FC between frontoparietal network (FPN) and other high-level neurocognitive networks (i.e. default mode network (DMN), affective network (AN), and salience network (SN)) as well as hyperconnectivity between SN seeds and the Rolandic operculum in SSN. In addition, compared with BA, SUD exhibited several distinct within- and between-network rs-FC alterations mainly involved in the DMN and FPN. Further, altered within- and between-network rs-FC showed significant association with clinical characteristics such as the severity of addiction in BA and duration of substance usage in SUD. The common rs-FC alterations in BA and SUD exhibited the relationship with consistent aberrant behaviors in both addiction groups, such as impaired inhibition control and salience attribution. By contrast, the distinct rs-FC alterations might suggest specific substance effects on the brain neural transmitter systems in SUD.

Identifying distinctive brain regions related to consumer choice behaviors on branded foods using activation likelihood estimation and machine learning

Introduction

Brand equity plays a crucial role in a brand's commercial success; however, research on the brain regions associated with brand equity has had mixed results. This study aimed to investigate key brain regions associated with the decision-making of branded and unbranded foods using quantitative neuroimaging meta-analysis and machine learning.

Methods

Quantitative neuroimaging meta-analysis was performed using the activation likelihood method. Activation of the ventral medial prefrontal cortex (VMPFC) overlapped between branded and unbranded foods. The lingual and parahippocampal gyri (PHG) were activated in the case of branded foods, whereas no brain regions were characteristically activated in response to unbranded foods. We proposed a novel predictive method based on the reported foci data, referencing the multi-voxel pattern analysis (MVPA) results. This approach is referred to as the multi-coordinate pattern analysis (MCPA). We conducted the MCPA, adopting the sparse partial least squares discriminant analysis (sPLS-DA) to detect unique brain regions associated with branded and unbranded foods based on coordinate data. The sPLS-DA is an extended PLS method that enables the processing of categorical data as outcome variables.

Results

We found that the lingual gyrus is a distinct brain region in branded foods. Thus, the VMPFC might be a core brain region in food categories in consumer behavior, regardless of whether they are branded foods. Moreover, the connection between the PHG and lingual gyrus might be a unique neural mechanism in branded foods.

Discussion

As this mechanism engages in imaging the feature-self based on emotionally subjective contextual associative memories, brand managers should create future-oriented relevancies between brands and consumers to build valuable brands.

Integrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder

Several lines of evidence support the involvement of transcriptomic and epigenetic mechanisms in the brain structural deficits of major depressive disorder (MDD) separately. However, research in these two areas has remained isolated. In this study, we proposed an integrative strategy that combined neuroimaging, brain-wide gene expression, and peripheral DNA methylation data to investigate the genetic basis of gray matter abnormalities in MDD. The MRI T1-weighted images and Illumina 850 K DNA methylation microarrays were obtained from 269 patients and 416 healthy controls, and brain-wide transcriptomic data were collected from Allen Human Brain Atlas. The between-group differences in gray matter volume (GMV) and differentially methylated CpG positions (DMPs) were examined. The genes with their expression patterns spatially related to GMV changes and genes with DMPs were overlapped and selected. Using principal component regression, the associations between DMPs in overlapped genes and GMV across individual patients were investigated, and the region-specific correlations between methylation status and gene expression were examined. We found significant associations between the decreased GMV and DMPs methylation status in the anterior cingulate cortex, inferior frontal cortex, and fusiform face cortex regions. These DMPs genes were primarily enriched in the neurodevelopmental and synaptic transmission process. There was a significant negative correlation between DNA methylation and gene expression in genes associated with GMV changes of the frontal cortex in MDD. Our findings suggest that GMV abnormalities in MDD may have a transcriptomic and epigenetic basis. This imaging-transcriptomic-epigenetic integrative analysis provides spatial and biological links between cortical morphological deficits and peripheral epigenetic signatures in MDD.

Improving the Eligibility of Task-Based fMRI Studies for Meta-Analysis: A Review and Reporting Recommendations

Decisions made during the analysis or reporting of an fMRI study influence the eligibility of that study to be entered into a meta-analysis. In a meta-analysis, results of different studies on the same topic are combined. To combine the results, it is necessary that all studies provide equivalent pieces of information. However, in task-based fMRI studies we see a large variety in reporting styles. Several specific meta-analysis methods have been developed to deal with the reporting practices occurring in task-based fMRI studies, therefore each requiring a specific type of input. In this manuscript we provide an overview of the meta-analysis methods and the specific input they require. Subsequently we discuss how decisions made during the study influence the eligibility of a study for a meta-analysis and finally we formulate some recommendations about how to report an fMRI study so that it complies with as many meta-analysis methods as possible.

Coordinate-Based Meta-Analyses of the Time Perception Network

The study of time perception has advanced over the past three decades to include numerous neuroimaging studies, most notably including the use of functional Magnetic Resonance Imaging (fMRI). Yet, with this increase in studies, there comes the desire to draw broader conclusions across datasets about the nature and instantiation of time in the human brain. In the absence of collating individual studies together, the field has employed the use of Coordinate-Based Meta-Analyses (CBMA), in which foci from individual studies are modeled as probability distributions within the brain, from which common areas of activation-likelihood are determined. This chapter provides an overview of these CBMA studies, the methods they employ, the conclusions drawn by them, and where future areas of inquiry lie. The result of this survey suggests the existence of a domain-general "timing network" that can be used both as a guide for individual neuroimaging studies and as a template for future meta-analyses.

Structural impairment in superficial and deep white matter in schizophrenia

OBJECTIVE

Although disconnectivity among brain regions has been one of the main hypotheses for schizophrenia, the superficial white matter (SWM) has received less attention in schizophrenia research than the deep white matter (DWM) owing to the challenge of consistent reconstruction across subjects.

METHODS

We obtained the diffusion magnetic resonance imaging (dMRI) data of 223 healthy controls and 143 patients with schizophrenia. After harmonising the raw dMRIs from three different studies, we performed whole-brain two-tensor tractography and fibre clustering on the tractography data. We compared the fractional anisotropy (FA) of white matter tracts between healthy controls and patients with schizophrenia. Spearman's rho was adopted for the associations with clinical symptoms measured by the Positive and Negative Syndrome Scale (PANSS). The Bonferroni correction was used to adjust multiple testing.

RESULTS

Among the 33 DWM and 8 SWM tracts, patients with schizophrenia had a lower FA in 14 DWM and 4 SWM tracts than healthy controls, with small effect sizes. In the patient group, the FA deviations of the corticospinal and superficial-occipital tracts were negatively correlated with the PANSS negative score; however, this correlation was not evident after adjusting for multiple testing.

CONCLUSION

We observed the structural impairments of both the DWM and SWM tracts in patients with schizophrenia. The SWM could be a potential target of interest in future research on neural biomarkers for schizophrenia.

Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.

CBMAT: a MATLAB toolbox for data preparation and post hoc analyses in neuroimaging meta-analyses

Coordinate-based meta-analysis (CBMA) is a powerful technique in the field of human brain imaging research. Due to its intense usage, several procedures for data preparation and post hoc analyses have been proposed so far. However, these steps are often performed manually by the researcher, and are therefore potentially prone to error and time-consuming. We hence developed the Coordinate-Based Meta-Analyses Toolbox (CBMAT) to provide a suite of user-friendly and automated MATLAB® functions allowing one to perform all these procedures in a fast, reproducible and reliable way. Besides the description of the code, in the present paper we also provide an annotated example of using CBMAT on a dataset including 34 experiments. CBMAT can therefore substantially improve the way data are handled when performing CBMAs. The code can be downloaded from https://github.com/Jordi-Manuello/CBMAT.git .

The brain activity pattern in alcohol-use disorders under inhibition response Task

BACKGROUND

Inhibitory control impairment in alcohol use disorder (AUD) may indicate detrimental effects of chronic alcohol use on different functional systems in the brain, but the current studies lack consistency. This study aims to identify the most consistent response inhibition-related brain dysfunction based on existing data.

METHODS

We performed systematic searches of the PubMed, Embase, Web of Science, and PsychINFO databases for available studies. Anisotropic effect-size signed differential mapping was used to quantitatively analyze the differences in response inhibition-related brain activation between AUD patients and HCs. Meta regression was used to explore the relationship between brain alterations and clinical variables.

RESULTS

The brain hypoactivation or hyperactivation in AUD patients compared with HCs during the response inhibition tasks was mainly located in the prefrontal cortex including the superior frontal gyrus, inferior frontal gyrus, and middle frontal gyrus, anterior cingulate gyrus (ACC), superior temporal gyrus, occipital gyrus, and somatosensory areas including postcentral gyrus and supramarginal gyrus. The meta-regression revealed that older patients were more likely to present activation in the left superior frontal gyrus when performing the response inhibition tasks.

CONCLUSIONS

The response inhibitive dysfunctions in a distinct prefrontal-cingulate cortices may presumably reflect the core impairment in cognitive control abilities. Dysfunction in the occipital gyrus and somatosensory areas may indicate an abnormal motor-sensory and visual function in AUD. Such functional abnormalities may represent neurophysiological correlates of the executive deficits observed in AUD patients. This study has been registered in PROSPERO (number CRD42022339384).

Imaging the Spin: Disentangling the Core Processes Underlying Mental Rotation by Network Mapping of Data From Meta-analysis

Research on the mental rotation task has sparked debate regarding the specific processes that underly the capability of humans to mentally rotate objects. The spread of reported brain activations suggests that mental rotation is subserved by a neural network circle. However, no common network has yet been found that uncovers the crucial processes underlying this ability. We aimed to identify the common network crucial for mental rotation by coordinate-based network mapping of previous neuroimaging findings in mental rotation. A meta-analysis revealed 710 peak activation coordinates from 42 fMRI studies in mental rotation, which include a total 844 participants. The coordinates were mapped to a normative functional connectome (n = 1000) to identify a network of connected regions. To account for experimental factors, we examined this network against two control tasks, action imitation and symbolic number processing. A common and crucial network for mental rotation, centring on dorsal premotor, superior parietal and inferior temporal lobes was revealed. This network, separated from other experimental aspects, suggests that the crucial processes underlying mental rotation are motor rotation, visuospatial processing, and higher order visual object recognition.

Revealing robust neural correlates of conscious and unconscious visual processing: activation likelihood estimation meta-analyses

Our ability to consciously perceive information from the visual scene relies on a myriad of intrinsic neural mechanisms. Functional neuroimaging studies have sought to identify the neural correlates of conscious visual processing and to further dissociate from those pertaining to preconscious and unconscious visual processing. However, delineating what core brain regions are involved in eliciting a conscious percept remains a challenge, particularly regarding the role of prefrontal-parietal regions. We performed a systematic search of the literature that yielded a total of 54 functional neuroimaging studies. We conducted two quantitative meta-analyses using activation likelihood estimation to identify reliable patterns of activation engaged by i. conscious (n = 45 studies, comprising 704 participants) and ii. unconscious (n = 16 studies, comprising 262 participants) visual processing during various task performances. Results of the meta-analysis specific to conscious percepts quantitatively revealed reliable activations across a constellation of regions comprising the bilateral inferior frontal junction, intraparietal sulcus, dorsal anterior cingulate, angular gyrus, temporo-occipital cortex and anterior insula. Neurosynth reverse inference revealed conscious visual processing to be intertwined with cognitive terms related to attention, cognitive control and working memory. Results of the meta-analysis on unconscious percepts revealed consistent activations in the lateral occipital complex, intraparietal sulcus and precuneus. These findings highlight the notion that conscious visual processing readily engages higher-level regions including the inferior frontal junction and unconscious processing reliably recruits posterior regions, mainly the lateral occipital complex.

Grey matter volume loss in Parkinson's disease psychosis and its relationship with serotonergic gene expression: A meta-analysis

BACKGROUND

Neuroanatomical alterations underlying psychosis in Parkinson's Disease (PDP) remain unclear. We carried out a meta-analysis of MRI studies investigating the neural correlates of PDP and examined its relation with dopaminergic and serotonergic receptor gene expression.

METHODS

PubMed, Web of Science and Embase were searched for MRI studies (k studies = 10) of PDP compared to PD patients without psychosis (PDnP). Seed-based d Mapping with Permutation of Subject Images and multiple linear regression analyses was used to examine the relationship between pooled estimates of grey matter volume (GMV) loss in PDP and D1/D2 and 5-HT1a/5-HT2a receptor gene expression estimates from Allen Human Brain Atlas.

RESULTS

We observed lower grey matter volume in parietal-temporo-occipital regions (PDP n = 211, PDnP, n = 298). GMV loss in PDP was associated with local expression of 5-HT1a (b = 0.109, p = 0.012) and 5-HT2a receptors (b= -0.106, p = 0.002) but not dopaminergic receptors.

CONCLUSION

Widespread GMV loss in the parieto-temporo-occipital regions may underlie PDP. Association between grey matter volume and local expression of serotonergic receptor genes may suggest a role for serotonergic receptors in PDP.

The impact of sociality and affective valence on brain activation: A meta-analysis

Thirty years of neuroimaging reveal the set of brain regions consistently associated with pleasant and unpleasant affect in humans-or the neural reference space for valence. Yet some of humans' most potent affective states occur in the context of other humans. Prior work has yet to differentiate how the neural reference space for valence varies as a product of the sociality of affective stimuli. To address this question, we meta-analyzed across 614 social and non-social affective neuroimaging contrasts, summarizing the brain regions that are consistently activated for social and non-social affective information. We demonstrate that across the literature, social and non-social affective stimuli yield overlapping activations within regions associated with visceromotor control, including the amygdala, hypothalamus, anterior cingulate cortex and insula. However, we find that social processing differs from non-social affective processing in that it involves additional cortical activations in the medial prefrontal and posterior cingulum that have been associated with mentalizing and prediction. A Bayesian classifier was able to differentiate unpleasant from pleasant affect, but not social from non-social affective states. Moreover, it was not able to classify unpleasantness from pleasantness at the highest levels of sociality. These findings suggest that highly social scenarios may be equally salient to humans, regardless of their valence.

Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study

Previous studies have explored resting-state functional connectivity (rs-FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency-specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age-matched and sex-matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed-based FC analyses in the conventional, slow-5, and slow-4 frequency bands at each site. Image-based meta-analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta-analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow-5 frequency band instead of the conventional and slow-4 frequency bands. The deep learning results showed that, compared with the conventional and slow-4 frequency bands, the slow-5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.

Are Brain Responses to Emotion a Reliable Endophenotype of Schizophrenia? An Image-Based Functional Magnetic Resonance Imaging Meta-analysis

BACKGROUND

Impaired emotion processing constitutes a key dimension of schizophrenia and a possible endophenotype of this illness. Empirical studies consistently report poorer emotion recognition performance in patients with schizophrenia as well as in individuals at enhanced risk of schizophrenia. Functional magnetic resonance imaging studies also report consistent patterns of abnormal brain activation in response to emotional stimuli in patients, in particular, decreased amygdala activation. In contrast, brain-level abnormalities in at-risk individuals are more elusive. We address this gap using an image-based meta-analysis of the functional magnetic resonance imaging literature.

METHODS

Functional magnetic resonance imaging studies investigating brain responses to negative emotional stimuli and reporting a comparison between at-risk individuals and healthy control subjects were identified. Frequentist and Bayesian voxelwise meta-analyses were performed separately, by implementing a random-effect model with unthresholded group-level T-maps from individual studies as input.

RESULTS

In total, 17 studies with a cumulative total of 677 at-risk individuals and 805 healthy control subjects were included. Frequentist analyses did not reveal significant differences between at-risk individuals and healthy control subjects. Similar results were observed with Bayesian analyses, which provided strong evidence for the absence of meaningful brain activation differences across the entire brain. Region of interest analyses specifically focusing on the amygdala confirmed the lack of group differences in this region.

CONCLUSIONS

These results suggest that brain activation patterns in response to emotional stimuli are unlikely to constitute a reliable endophenotype of schizophrenia. We suggest that future studies instead focus on impaired functional connectivity as an alternative and promising endophenotype.

Disrupted functional connectivity of the brain reward system in substance use problems: A meta-analysis of functional neuroimaging studies

Extensive literature suggests that the brain reward system is crucial in understanding the neurobiology of substance use disorders. However, evidence of reliable deficits in functional connectivity across studies on substance use problems remains limited. Therefore, a voxel-wise seed-based meta-analysis using brain regions of the reward system as seeds of interest was conducted on 96 studies representing 5757 subjects with substance use problems. The ventromedial prefrontal cortex exhibited hyperconnectivity with the ventral striatum and hypoconnectivity with the amygdala and hippocampus. The executive striatum showed hyperconnectivity with the motor thalamus and dorsolateral prefrontal cortex and hypoconnectivity with the anterior cingulate cortex and anterior insula. Finally, the limbic striatum was found to be hyperconnected to the orbitofrontal cortex and hypoconnected to the precuneus compared with healthy subjects. The current study provided meta-analytical evidence of deficient functional connectivity between brain regions of the reward system and cortico-striato-thalamocortical loops in addiction. These results are consistent with deficits in motivation and habit formation occurring in addiction, and they highlight alterations in brain regions involved in socio-emotional processing and attention salience.

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.

Neural systems underlying RDoC social constructs: An activation likelihood estimation meta-analysis

Neuroscientists have sought to identify the underlying neural systems supporting social processing that allow interaction and communication, forming social relationships, and navigating the social world. Through the use of NIMH's Research Domain Criteria (RDoC) framework, we evaluated consensus among studies that examined brain activity during social tasks to elucidate regions comprising the "social brain". We examined convergence across tasks corresponding to the four RDoC social constructs, including Affiliation and Attachment, Social Communication, Perception and Understanding of Self, and Perception and Understanding of Others. We performed a series of coordinate-based meta-analyses using the activation likelihood estimate (ALE) method. Meta-analysis was performed on whole-brain coordinates reported from 864 fMRI contrasts using the NiMARE Python package, revealing convergence in medial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, temporoparietal junction, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. Additionally, four separate RDoC-based meta-analyses revealed differential convergence associated with the four social constructs. These outcomes highlight the neural support underlying these social constructs and inform future research on alterations among neurotypical and atypical populations.

Risk-taking in the human brain: An activation likelihood estimation meta-analysis of the balloon analog risk task (BART)

The Balloon Analog Risk Task (BART) is increasingly used to assess risk-taking behavior and brain function. However, the brain networks underlying risk-taking during the BART and its reliability remain controversial. Here, we combined the activation likelihood estimation (ALE) meta-analysis with both task-based and task-free functional connectivity (FC) analysis to quantitatively synthesize brain networks involved in risk-taking during the BART, and compared the differences between adults and adolescents studies. Based on 22 pooled publications, the ALE meta-analysis revealed multiple brain regions in the reward network, salience network, and executive control network underlying risk-taking during the BART. Compared with adult risk-taking, adolescent risk-taking showed greater activation in the insula, putamen, and prefrontal regions. The combination of meta-analytic connectivity modeling with task-free FC analysis further confirmed the involvement of the reward, salience, and cognitive control networks in the BART. These findings demonstrate the core brain networks for risk-taking during the BART and support the utility of the BART for future neuroimaging and developmental research.

Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks

BACKGROUND

Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.

AIMS

To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.

METHOD

We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.

RESULTS

We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19-85.74%; sensitivity, 75.31-89.29% and area under the receiver operating characteristic curve, 0.797-0.909.

CONCLUSIONS

These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

Functional hybrid factor regression model for handling heterogeneity in imaging studies

This paper develops a functional hybrid factor regression modelling framework to handle the heterogeneity of many large-scale imaging studies, such as the Alzheimer's disease neuroimaging initiative study. Despite the numerous successes of those imaging studies, such heterogeneity may be caused by the differences in study environment, population, design, protocols or other hidden factors, and it has posed major challenges in integrative analysis of imaging data collected from multicentres or multistudies. We propose both estimation and inference procedures for estimating unknown parameters and detecting unknown factors under our new model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed procedures is assessed by using Monte Carlo simulations and a real data example on hippocampal surface data from the Alzheimer's disease study.

Neural mechanisms of brand love relationship dynamics: Is the development of brand love relationships the same as that of interpersonal romantic love relationships?

Brand love is a relationship between brands and consumers. Managing the relationship is an important issue for marketing strategy since it changes according to temporal flow. Brand love theories, including their dynamics, have been developed based on interpersonal romantic love theories. Although many brand love studies have provided useful findings, the neural mechanism of brand love remains unclear. Especially, its dynamics have not been considered from a neuroscience perspective. The present study addressed the commonalities and differentiations of activated brain regions between brand love and interpersonal romantic love relationships using a quantitative neuroimaging meta-analytic approach, from the view of brain connectivity. Regarding the mental processes of each love relationship related to these activated brain regions, decoding analysis was conducted using the NeuroQuery platform to prevent reverse inference. The results revealed that different neural mechanisms and mental processes were distinctively involved in the dynamics of each love relationship, although the anterior insula overlapped across all stages and the reinforcement learning system was driven between both love relationships in the early stage. Remarkably, regarding the distinctive mental processes, although prosocial aspects were involved in the mental processes of interpersonal romantic love relationships across all stages, they were not involved in the mental processes of brand love relationships. Conclusively, although common brain regions and mental processes between both love relationships were observed, neural mechanisms and mental processes in brand love relationship dynamics might be innately different from those in the interpersonal romantic love relationship dynamics. As this finding indicates essential distinctiveness between both these relationships, theories concerning interpersonal romantic love should be applied cautiously when investigating brand love relationship dynamics.

Disrupted local functional connectivity in schizophrenia: An updated and extended meta-analysis

Neuroimaging studies have shown that schizophrenia is associated with disruption of resting-state local functional connectivity. However, these findings vary considerably, which hampers our understanding of the underlying pathophysiological mechanisms of schizophrenia. Here, we performed an updated and extended meta-analysis to identify the most consistent changes of local functional connectivity measured by regional homogeneity (ReHo) in schizophrenia. Specifically, a systematic search of ReHo studies in patients with schizophrenia in PubMed, Embase, and Web of Science identified 18 studies (20 datasets), including 652 patients and 596 healthy controls. In addition, we included three whole-brain statistical maps of ReHo differences calculated based on independent datasets (163 patients and 194 controls). A voxel-wise meta-analysis was then conducted to investigate ReHo alterations and their relationship with clinical characteristics using the newly developed seed-based d mapping with permutation of subject images (SDM-PSI) meta-analytic approach. Compared with healthy controls, patients with schizophrenia showed significantly higher ReHo in the bilateral medial superior frontal gyrus, while lower ReHo in the bilateral postcentral gyrus, right precentral gyrus, and right middle occipital gyrus. The following sensitivity analyses including jackknife analysis, subgroup analysis, heterogeneity test, and publication bias test demonstrated that our results were robust and highly reliable. Meta-regression analysis revealed that illness duration was negatively correlated with ReHo abnormalities in the right precentral/postcentral gyrus. This comprehensive meta-analysis not only identified consistent and reliably aberrant local functional connectivity in schizophrenia but also helped to further deepen our understanding of its pathophysiology.

Convergent gray matter alterations across drugs of abuse and network-level implications: A meta-analysis of structural MRI studies

BACKGROUND

Neuroimaging studies often consider brain alterations linked with substance abuse within the context of individual drugs (e.g., nicotine), while neurobiological theories of addiction emphasize common brain network-level alterations across drug classes. Using emergent meta-analytic techniques, we identified common structural brain alterations across drugs and characterized the functionally-connected networks with which such structurally altered regions interact.

METHODS

We identified 82 articles characterizing gray matter (GM) volume differences for substance users vs. controls. Using the anatomical likelihood estimation algorithm, we identified convergent GM reductions across drug classes. Next, we performed resting-state and meta-analytic functional connectivity analyses using each structurally altered region as a seed and computed whole-brain functional connectivity profiles as the union of both maps. We characterized an "extended network" by identifying brain areas demonstrating the highest degree of functional coupling with structurally impacted regions. Finally, hierarchical clustering was performed leveraging extended network nodes' functional connectivity profiles to delineate subnetworks.

RESULTS

Across drug classes, we identified medial frontal/ventromedial prefrontal, and multiple regions in anterior cingulate (ACC) and insula as regions displaying convergent GM reductions among users. Overlap of these regions' functional connectivity profiles identified ACC, inferior frontal, PCC, insula, superior temporal, and putamen as regions of an impacted extended network. Hierarchical clustering revealed 3 subnetworks closely corresponding to default mode (PCC, angular), salience (dACC, caudate), and executive control networks (dlPFC and parietal).

CONCLUSIONS

These outcomes suggest that substance-related structural brain alterations likely have implications for the functioning of canonical large-scale networks and the perpetuation of substance use and neurocognitive alterations.

Seeking Overlapping Neuroanatomical Alterations between Dyslexia and Attention-Deficit/Hyperactivity Disorder: A Meta-Analytic Replication Study

The present work is a replication article based on the paper “Are there shared neural correlates between dyslexia and ADHD? A meta-analysis of voxel-based morphometry studies” by McGrath and Stoodley (2019). In the original research, the authors used activation likelihood estimation (ALE), a technique to perform coordinate-based meta-analysis (CBMA), to investigate the existence of brain regions undergoing gray matter alteration in association with both attention-deficit/hyper-activity disorder (ADHD) and dyslexia. Here, the same voxel-based morphometry dataset was analyzed, while using the permutation-subject images version of signed differential mapping (PSI-SDM) in place of ALE. Overall, the replication converged with the original paper in showing a limited overlap between the two conditions. In particular, no significant effect was found for dyslexia, therefore precluding any form of comparison between the two disorders. The possible influences of biological sex, age, and medication status were also ruled out. Our findings are in line with literature about gray matter alteration associated with ADHD and dyslexia, often showing conflicting results. Therefore, although neuropsychological and clinical evidence suggest some convergence between ADHD and dyslexia, more future research is sorely needed to reach a consensus on the neuroimaging domain in terms of patterns of gray matter alteration.

Spontaneous brain activity abnormalities in migraine: A meta-analysis of functional neuroimaging

Neuroimaging studies have demonstrated that migraine is accompanied by spontaneous brain activity alterations in specific regions. However, these findings are inconsistent, thus hindering our understanding of the potential neuropathology. Hence, we performed a quantitative whole-brain meta-analysis of relevant resting-state functional imaging studies to identify brain regions consistently involved in migraine. A systematic search of studies that investigated the differences in spontaneous brain activity patterns between migraineurs and healthy controls up to April 2022 was conducted. We then performed a whole-brain voxel-wise meta-analysis using the anisotropic effect size version of seed-based d mapping software. Complementary analyses including jackknife sensitivity analysis, heterogeneity test, publication bias test, subgroup analysis, and meta-regression analysis were conducted as well. In total, 24 studies that reported 31 datasets were finally eligible for our meta-analysis, including 748 patients and 690 controls. In contrast to healthy controls, migraineurs demonstrated consistent and robust decreased spontaneous brain activity in the angular gyrus, visual cortex, and cerebellum, while increased activity in the caudate, thalamus, pons, and prefrontal cortex. Results were robust and highly replicable in the following jackknife sensitivity analysis and subgroup analysis. Meta-regression analyses revealed that a higher visual analog scale score in the patient sample was associated with increased spontaneous brain activity in the left thalamus. These findings provided not only a comprehensive overview of spontaneous brain activity patterns impairments, but also useful insights into the pathophysiology of dysfunction in migraine.

How reliable are amygdala findings in psychopathy? A systematic review of MRI studies

The amygdala is a key component in predominant neural circuitry models of psychopathy. Yet, after two decades of neuroimaging research on psychopathy, the reproducibility of amygdala findings is questionable. We systematically reviewed MRI studies (81 of adults, 53 of juveniles) to determine the consistency of amygdala findings across studies, as well as within specific types of experimental tasks, community versus forensic populations, and the lowest- versus highest-powered studies. Three primary findings emerged. First, the majority of studies found null relationships between psychopathy and amygdala structure and function, even in the context of theoretically relevant tasks. Second, findings of reduced amygdala activity were more common in studies with low compared to high statistical power. Third, the majority of peak coordinates of reduced amygdala activity did not fall primarily within the anatomical bounds of the amygdala. Collectively, these findings demonstrate significant gaps in the empirical support for the theorized role of the amygdala in psychopathy and indicate the need for novel research perspectives and approaches in this field.

Extended functional connectivity of convergent structural alterations among individuals with PTSD: a neuroimaging meta-analysis

Background

Post-traumatic stress disorder (PTSD) is a debilitating disorder defined by the onset of intrusive, avoidant, negative cognitive or affective, and/or hyperarousal symptoms after witnessing or experiencing a traumatic event. Previous voxel-based morphometry studies have provided insight into structural brain alterations associated with PTSD with notable heterogeneity across these studies. Furthermore, how structural alterations may be associated with brain function, as measured by task-free and task-based functional connectivity, remains to be elucidated.

Methods

Using emergent meta-analytic techniques, we sought to first identify a consensus of structural alterations in PTSD using the anatomical likelihood estimation (ALE) approach. Next, we generated functional profiles of identified convergent structural regions utilizing resting-state functional connectivity (rsFC) and meta-analytic co-activation modeling (MACM) methods. Finally, we performed functional decoding to examine mental functions associated with our ALE, rsFC, and MACM brain characterizations.

Results

We observed convergent structural alterations in a single region located in the medial prefrontal cortex. The resultant rsFC and MACM maps identified functional connectivity across a widespread, whole-brain network that included frontoparietal and limbic regions. Functional decoding revealed overlapping associations with attention, memory, and emotion processes.

Conclusions

Consensus-based functional connectivity was observed in regions of the default mode, salience, and central executive networks, which play a role in the tripartite model of psychopathology. Taken together, these findings have important implications for understanding the neurobiological mechanisms associated with PTSD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12993-022-00196-2.

Altered resting state dynamic functional connectivity of amygdala subregions in patients with autism spectrum disorder: A multi-site fMRI study

BACKGROUND

Autism spectrum disorder (ASD) is associated with altered brain connectivity. Previous studies have focused on the static functional connectivity pattern from amygdala subregions in ASD while ignoring its dynamics. Considering that dynamic functional connectivity (dFC) can provide different perspectives, the present study aims to investigate the dFC pattern of the amygdala subregions in ASD patients.

METHODS

Data of 618 ASD patients and 836 typical controls (TCs) of 30 sites were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The sliding window approach was applied to conduct seed-based dFC analysis. The seed regions were bilateral basolateral (BLA) and centromedial-superficial amygdala (CSA). A two-sample t-test was done at each site. Image-based meta-analysis (IBMA) based on the results from all sites was performed. Correlation analysis was conducted between the dFC values and the clinical scores.

RESULTS

The ASD patients showed lower dFC between the left BLA and the bilateral inferior temporal (ITG)/left superior frontal gyrus, between the right BLA and right ITG/right thalamus/left superior temporal gyrus, and between the right CSA and middle temporal gyrus. The ASD patients showed higher dFC between the left BLA and temporal lobe/right supramarginal gyrus, between the right BLA and left calcarine gyrus, and between the left CSA and left calcarine gyrus. Correlation analysis revealed that the symptom severity was positively correlated with the dFC between the bilateral BLA and ITG in ASD.

CONCLUSIONS

Abnormal dFC of the specific amygdala subregions may provide new insights into the pathological mechanisms of ASD.

The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study

Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research.

Evidence for non-selective response inhibition in uncertain contexts revealed by combined meta-analysis and Bayesian analysis of fMRI data

Response inhibition is typically considered a brain mechanism selectively triggered by particular “inhibitory” stimuli or events. Based on recent research, an alternative non-selective mechanism was proposed by several authors. Presumably, the inhibitory brain activity may be triggered not only by the presentation of “inhibitory” stimuli but also by any imperative stimuli, including Go stimuli, when the context is uncertain. Earlier support for this notion was mainly based on the absence of a significant difference between neural activity evoked by equiprobable Go and NoGo stimuli. Equiprobable Go/NoGo design with a simple response time task limits potential confounds between response inhibition and accompanying cognitive processes while not preventing prepotent automaticity. However, previous neuroimaging studies used classical null hypothesis significance testing, making it impossible to accept the null hypothesis. Therefore, the current research aimed to provide evidence for the practical equivalence of neuronal activity in the Go and NoGo trials using Bayesian analysis of functional magnetic resonance imaging (fMRI) data. Thirty-four healthy participants performed a cued Go/NoGo task with an equiprobable presentation of Go and NoGo stimuli. To independently localize brain areas associated with response inhibition in similar experimental conditions, we performed a meta-analysis of fMRI studies using equal-probability Go/NoGo tasks. As a result, we observed overlap between response inhibition areas and areas that demonstrate the practical equivalence of neuronal activity located in the right dorsolateral prefrontal cortex, parietal cortex, premotor cortex, and left inferior frontal gyrus. Thus, obtained results favour the existence of non-selective response inhibition, which can act in settings of contextual uncertainty induced by the equal probability of Go and NoGo stimuli.

Six actions to improve detection of critical features for neuroimaging coordinate-based meta-analysis preparation

Coordinate-based meta-analysis (CBMA) is a research strategy widely used in the field of human brain imaging. Although dedicated tools as BrainMap or Neurosynth had been developed in past years, some of the crucial steps necessary to identify and compose the dataset are still user-based, resulting in a not standardized approach to literature search, as well as in time-consuming and prone to errors procedures. In particular, this concern involves the assessment of voxel-wise whole brain analyses in contrast to ROI-based ones, and the identification of available lists of peaks of effect (i.e., x,y,z coordinates of the foci). Here, we propose six simple actions that can be undertaken by any researcher and by the publishing system, allowing to limit the risk of erroneous decisions on the inclusion of experimental data in the meta-analytic dataset. This straightforward and useful strategy would reduce possible bias in CBMA, therefore allowing to obtain more reliable results.

Learning under social versus nonsocial uncertainty: A meta-analytic approach

Much of the uncertainty that clouds our understanding of the world springs from the covert values and intentions held by other people. Thus, it is plausible that specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. To test this hypothesis, we scoured academic databases for neuroimaging studies involving learning under uncertainty, and performed a meta‐analysis of brain activation maps that compared learning in the face of social versus nonsocial uncertainty. Although most of the brain activations associated with learning error signals were shared between social and nonsocial conditions, we found some evidence for functional segregation of error signals of exclusively social origin during learning in limited regions of ventrolateral prefrontal cortex and insula. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas processing generic value representation and learning, but that a specialized circuitry might have evolved in prefrontal regions to deal with social context representation and strategic action.