Abnormalities of cerebral blood flow and the regional brain function in Parkinson's disease: a systematic review and multimodal neuroimaging meta-analysis

Background

Parkinson's disease (PD) is a neurodegenerative disease with high incidence rate. Resting state functional magnetic resonance imaging (rs-fMRI), as a widely used method for studying neurodegenerative diseases, has not yet been combined with two important indicators, amplitude low-frequency fluctuation (ALFF) and cerebral blood flow (CBF), for standardized analysis of PD.

Methods

In this study, we used seed-based d-mapping and permutation of subject images (SDM-PSI) software to investigate the changes in ALFF and CBF of PD patients. After obtaining the regions of PD with changes in ALFF or CBF, we conducted a multimodal analysis to identify brain regions where ALFF and CBF changed together or could not synchronize.

Results

The final study included 31 eligible trials with 37 data sets. The main analysis results showed that the ALFF of the left striatum and left anterior thalamic projection decreased in PD patients, while the CBF of the right superior frontal gyrus decreased. However, the results of multimodal analysis suggested that there were no statistically significant brain regions. In addition, the decrease of ALFF in the left striatum and the decrease of CBF in the right superior frontal gyrus was correlated with the decrease in clinical cognitive scores.

Conclusion

PD patients had a series of spontaneous brain activity abnormalities, mainly involving brain regions related to the striatum-thalamic-cortex circuit, and related to the clinical manifestations of PD. Among them, the left striatum and right superior frontal gyrus are more closely related to cognition.

Systematic review registration

https://www.crd.york.ac.uk/ PROSPERO (CRD42023390914).

Increased Cerebellar Gray Matter Volume in Athletes: A Voxel-Wise Coordinate-Based Meta-Analysis

Purpose: The purpose of this systematic review and meta-analysis study was to investigate distinct brain structural characteristics in athletes as compared with those in non-athletes by quantifying regional gray matter (GM) volume changes using voxel-based morphometry analysis based on a whole-brain approach. Methods: The systematic literature search was conducted from November 1, 2020 to October 18, 2021 via the two search engines including the PubMed and Web of Science. We included 13 studies that reported GM volume data in 229 athletes as compared 219 non-athletes based on the whole-brain analysis with specific three-dimensional coordinates in a standard stereotactic space. Thus, we performed a coordinate-based meta-analysis using the seed-based d mapping via permutation of subject images methods. Result: The coordinate-based meta-analysis reported that the athletes significantly reveal greater regional GM volume across right cerebellar lobules IV-V and Brodmann area 37 regions than those in the non-athletes with minimal levels of heterogeneity and publication bias between the included studies. The subgroup analyses show that greater GM volume for athletes in closed-skill sports appeared across the right cerebellar hemispheric lobules VIII and the right cingulum than those for non-athletes. Conclusion: These cumulative findings from multiple brain imaging studies suggest potential brain plasticity evidence in the athletes who experienced extensive motor training.

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

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

Success versus failure in cognitive control: meta-analytic evidence from neuroimaging studies on error processing

Brain mechanisms of error processing have often been investigated using response interference tasks and focusing on the posterior medial frontal cortex, which is also implicated in resolving response conflict in general. Thereby, the role other brain regions may play has remained undervalued. Here, activation likelihood estimation meta-analyses were used to synthesize the neuroimaging literature on brain activity related to committing errors versus responding successfully in interference tasks and to test for commonalities and differences. The salience network and the temporoparietal junction were commonly recruited irrespective of whether responses were correct or incorrect, pointing towards a general involvement in coping with situations that call for increased cognitive control. The dorsal posterior cingulate cortex, posterior thalamus, and left superior frontal gyrus showed error-specific convergence, which underscores their consistent involvement when performance goals are not met. In contrast, successful responding revealed stronger convergence in the dorsal attention network and lateral prefrontal regions. Underrecruiting these regions in error trials may reflect failures in activating the task-appropriate stimulus-response contingencies necessary for successful response execution.

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.

Neuroimaging alterations in dementia with Lewy bodies and neuroimaging differences between dementia with Lewy bodies and Alzheimer's disease: An activation likelihood estimation meta-analysis

Aims

The aim of this study was to identify brain regions with local, structural, and functional abnormalities in dementia with Lewy bodies (DLB) and uncover the differences between DLB and Alzheimer's disease (AD). The neural networks involved in the identified abnormal brain regions were further described.

Methods

PubMed, Web of Science, OVID, Science Direct, and Cochrane Library databases were used to identify neuroimaging studies that included DLB versus healthy controls (HCs) or DLB versus AD. The coordinate‐based meta‐analysis and functional meta‐analytic connectivity modeling were performed using the activation likelihood estimation algorithm.

Results

Eleven structural studies and fourteen functional studies were included in this quantitative meta‐analysis. DLB patients showed a dysfunction in the bilateral inferior parietal lobule and right lingual gyrus compared with HC patients. DLB patients showed a relative preservation of the medial temporal lobe and a tendency of lower metabolism in the right lingual gyrus compared with AD. The frontal‐parietal, salience, and visual networks were all abnormally co‐activated in DLB, but the default mode network remained normally co‐activated compared with AD.

Conclusions

The convergence of local brain regions and co‐activation neural networks might be potential specific imaging markers in the diagnosis of DLB. This might provide a pathway for the neural regulation in DLB patients, and it might contribute to the development of specific interventions for DLB and AD.

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

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

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

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

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

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

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

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

Cortical thickness in Parkinson's disease: a coordinate-based meta-analysis

Parkinson's disease (PD) is a common age-related neurodegenerative disease that affects the structural architecture of the cerebral cortex. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis is a popular measure to assess brain structural alterations in the gray matter in PD. However, the results of CTh analysis in PD lack consistency and have not been systematically reviewed. We conducted a comprehensive coordinate-based meta-analysis (CBMA) of 38 CTh studies (57 comparison datasets) in 1,843 patients with PD using the latest seed-based d mapping software. Compared with 1,172 healthy controls, no significantly consistent CTh alterations were found in patients with PD, suggesting CTh as an unreliable neuroimaging marker for PD. The lack of consistent CTh alterations in PD could be ascribed to the heterogeneity in clinical populations, variations in imaging methods, and underpowered small sample sizes. These results highlight the need to control for potential confounding factors to produce robust and reproducible CTh results in PD.

Cortical Thickness in Migraine: A Coordinate-Based Meta-Analysis

Cortical thickness (CTh) via surface-based morphometry analysis is a popular method to characterize brain morphometry. Many studies have been performed to investigate CTh abnormalities in migraine. However, the results from these studies were not consistent and even conflicting. These divergent results hinder us to obtain a clear picture of brain morphometry regarding CTh alterations in migraine. Coordinate-based meta-analysis (CBMA) is a promising technique to quantitatively pool individual neuroimaging studies to identify consistent brain areas involved. Electronic databases (PubMed, EMBASE, Web of Science, China National Knowledge Infrastructure, WanFang, and SinoMed) and other sources (bioRxiv and reference lists of relevant articles and reviews) were systematically searched for studies that compared regional CTh differences between patients with migraine and healthy controls (HCs) up to May 15, 2020. A CBMA was performed using the Seed-based d Mapping with Permutation of Subject Images approach. In total, we identified 16 studies with 17 datasets reported that were eligible for the CBMA. The 17 datasets included 872 patients with migraine (average sample size 51.3, mean age 39.6 years, 721 females) and 949 HCs (average sample size 59.3, mean age 44.2 years, 680 females). The CBMA detected no statistically significant consistency of CTh alterations in patients with migraine relative to HCs. Sensitivity analysis and subgroup analysis verified this result to be robust. Metaregression analyses revealed that this CBMA result was not confounded by age, gender, aura, attack frequency per month, and illness duration. Our CBMA adds to the evidence of the replication crisis in neuroimaging research that is increasingly recognized. Many potential confounders, such as underpowered sample size, heterogeneous patient selection criteria, and differences in imaging collection and methodology, may contribute to the inconsistencies of CTh alterations in migraine, which merit attention before planning future research on this topic.

Localised Grey Matter Atrophy in Multiple Sclerosis and Clinically Isolated Syndrome-A Coordinate-Based Meta-Analysis, Meta-Analysis of Networks, and Meta-Regression of Voxel-Based Morphometry Studies

Background: Atrophy of grey matter (GM) is observed in the earliest stages of multiple sclerosis (MS) and is associated with cognitive decline and physical disability. Localised GM atrophy in MS can be explored and better understood using magnetic resonance imaging and voxel-based morphometry (VBM). However, results are difficult to interpret due to methodological differences between studies. Methods: Coordinate-based analysis is a way to find the reliably observable results across multiple independent VBM studies. This work uses coordinate-based meta-analysis, meta-analysis of networks, and meta-regression to summarise the evidence from voxel-based morphometry of regional GM hanges in patients with MS and clinically isolated syndrome (CIS), and whether these measured changes are relatable to clinical features. Results: Thirty-four published articles reporting forty-four independent experiments using VBM for the assessment of GM atrophy between MS or CIS patients and healthy controls were identified. Analysis identified eight clusters of consistent cross-study reporting of localised GM atrophy involving both cortical and subcortical regions. Meta-network analysis identified a network-like pattern indicating that GM loss occurs with some symmetry between hemispheres. Meta-regression analysis indicates a relationship between disease duration or age and the magnitude of reported statistical effect in some deep GM structures. Conclusions: These results suggest consistency in MRI-detectible regional GM loss across multiple MS studies, and the estimated effect sizes and symmetries can help design prospective studies to test specific hypotheses.

Cortical thickness in chronic pain: A protocol for systematic review and meta-analysis

BACKGROUND

Numerous studies using a variety of non-invasive neuroimaging techniques in vivo have demonstrated that chronic pain (CP) is associated with brain alterations. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis of magnetic resonance imaging data is a valid and sensitive method to investigate the structure of brain gray matter. Many studies have employed SBM to measure CTh difference between patients with CP and pain-free controls and provided important insights into the brain basis of CP. However, the findings from these studies were inconsistent and have not been quantitatively reviewed.

METHODS

Three major electronic medical databases: PubMed, Web of Science, and Embase were searched for eligible studies published in English on April 3, 2020. This protocol was prepared based on the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. The Seed-based d Mapping with Permutation of Subject Images software package will be employed to conducted a coordinate-based meta-analysis (CBMA) to identify consistent CTh differences between patients with CP and pain-free controls. Several complementary analyses, including sensitivity analysis, heterogeneity analysis, publication bias, subgroup analysis, and meta-regression analysis, will be further conducted to test the robustness of the results.

RESULTS

This CBMA will tell us whether CP with different subtypes shares common CTh alterations and what the pattern of its characterized alterations is.

CONCLUSIONS

To the best of our knowledge, this will be the first CBMA of SBM studies that characterizes brain CTh alterations in CP. The CBMA will provide the quantitative evidence of common brain cortical morphometry of CP. The findings will help us to understand the neural basis underlying CP.

TRIAL REGISTRATION NUMBER

INPLASY202050069.

Altered local connectivity in chronic pain: A voxel-wise meta-analysis of resting-state functional magnetic resonance imaging studies

BACKGROUND

A number of studies have used regional homogeneity (ReHo) to depict local functional connectivity in chronic pain (CP). However, the findings from these studies were mixed and inconsistent.

METHODS

A computerized literature search will be performed in PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), WanFang, and SinoMed databases until June 15, 2019 and updated on March 20, 2020. This protocol will follow the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P). The Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software will be used for this voxel-wise meta-analysis.

RESULTS

This meta-analysis will identify the most consistent ReHo alterations in CP.

CONCLUSIONS

To our knowledge, this will be the first voxel-wise meta-analysis that integrates ReHo findings in CP. This meta-analysis will offer the quantitative evidence of ReHo alterations that characterize brain local functional connectivity of CP.

PROSPERO REGISTRATION NUMBER

CRD42019148523.

Cortical thickness in Parkinson disease: A coordinate-based meta-analysis

BACKGROUND

A growing number of studies have used surface-based morphometry (SBM) analyses to investigate gray matter cortical thickness (CTh) abnormalities in Parkinson disease (PD). However, the results across studies are inconsistent and have not been systematically reviewed. A clear picture of CTh alterations in PD remains lacked. Coordinate-based meta-analysis (CBMA) is a powerful tool to quantitatively integrate the results of individual voxel-based neuroimaging studies to identify the functional or structural neural substrates of particular neuropsychiatric disorders. Recently, CBMA has been updated for integrating SBM studies.

METHODS

The online databases PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), WanFang, and SinoMed were comprehensively searched without language limitations from the database inception to February 2, 2020. We will include all SBM studies that compared regional CTh between patients with idiopathic PD and healthy control subjects at the whole-cortex level using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI). In addition to the main CBMA, we will conduct several supplementary analyses to test the robustness of the results, such as jackknife analyses, subgroup analyses, heterogeneity analyses, publication bias analyses, and meta-regression analyses.

RESULTS

This CBMA will offer the latest evidence of CTh alterations in PD.

CONCLUSIONS

Consistent and robust evidence of CTh alterations will feature brain morphometry of PD and may facilitate biomarker development.

PROSPERO REGISTRATION NUMBER

CRD42020148775.

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

BACKGROUND

Voxel-based morphometry (VBM) is an objective structural magnetic resonance imaging (MRI) technique which allows researchers to investigate group-level differences in regional gray matter (GM) volume or density over the whole brain. In the last decade, VBM studies in restless leg syndrome (RLS) have exhibited inconsistent and conflicting findings.

METHODS

Studies will be identified through a computerized literature search of the following databases: PubMed, Web of Science, and Embase until October 1, 2018 and updated on March 1, 2020. This protocol will be performed in accordance with the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P). In addition, we will follow the recent guidelines and recommendations for coordinate-based meta-analysis (CBMA). This CBMA will be performed with the seed-based d mapping with permutation of subject images (SDM-PSI) software.

RESULTS

This CBMA will offer the latest evidence of GM alterations in RLS.

CONCLUSIONS

To our knowledge, this will be the first CBMA that pooled VBM findings in RLS. This quantitative evidence of GM alterations will characterize brain morphometry of RLS.

PROSPERO REGISTRATION NUMBER

CRD42018117014.

Clustering the Brain With "CluB": A New Toolbox for Quantitative Meta-Analysis of Neuroimaging Data

In this paper we describe and validate a new coordinate-based method for meta-analysis of neuroimaging data based on an optimized hierarchical clustering algorithm: CluB (Clustering the Brain). The CluB toolbox permits both to extract a set of spatially coherent clusters of activations from a database of stereotactic coordinates, and to explore each single cluster of activation for its composition according to the cognitive dimensions of interest. This last step, called “cluster composition analysis,” permits to explore neurocognitive effects by adopting a factorial-design logic and by testing the working hypotheses using either asymptotic tests, or exact tests either in a classic inference, or in a Bayesian-like context. To perform our validation study, we selected the fMRI data from 24 normal controls involved in a reading task. We run a standard random-effects second level group analysis to obtain a “Gold Standard” of reference. In a second step, the subject-specific reading effects (i.e., the linear t-contrast “reading > baseline”) were extracted to obtain a coordinates-based database that was used to run a meta-analysis using both CluB and the popular Activation Likelihood Estimation method implemented in the software GingerALE. The results of the two meta-analyses were compared against the “Gold Standard” to compute performance measures, i.e., sensitivity, specificity, and accuracy. The GingerALE method obtained a high level of accuracy (0.967) associated with a high sensitivity (0.728) and specificity (0.971). The CluB method obtained a similar level of accuracy (0.956) and specificity (0.969), notwithstanding a lower level of sensitivity (0.14) due to the lack of prior Gaussian transformation of the data. Finally, the two methods obtained a good-level of concordance (AC₁ = 0.93). These results suggested that methods based on hierarchical clustering (and post-hoc statistics) and methods requiring prior Gaussian transformation of the data can be used as complementary tools, with the GingerALE method being optimal for neurofunctional mapping of pooled data according to simpler designs, and the CluB method being preferable to test more specific, and localized, neurocognitive hypotheses according to factorial designs.

Brain-based ranking of cognitive domains to predict schizophrenia

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

The Influence of Study-Level Inference Models and Study Set Size on Coordinate-Based fMRI Meta-Analyses

Given the increasing amount of neuroimaging studies, there is a growing need to summarize published results. Coordinate-based meta-analyses use the locations of statistically significant local maxima with possibly the associated effect sizes to aggregate studies. In this paper, we investigate the influence of key characteristics of a coordinate-based meta-analysis on (1) the balance between false and true positives and (2) the activation reliability of the outcome from a coordinate-based meta-analysis. More particularly, we consider the influence of the chosen group level model at the study level [fixed effects, ordinary least squares (OLS), or mixed effects models], the type of coordinate-based meta-analysis [Activation Likelihood Estimation (ALE) that only uses peak locations, fixed effects, and random effects meta-analysis that take into account both peak location and height] and the amount of studies included in the analysis (from 10 to 35). To do this, we apply a resampling scheme on a large dataset (N = 1,400) to create a test condition and compare this with an independent evaluation condition. The test condition corresponds to subsampling participants into studies and combine these using meta-analyses. The evaluation condition corresponds to a high-powered group analysis. We observe the best performance when using mixed effects models in individual studies combined with a random effects meta-analysis. Moreover the performance increases with the number of studies included in the meta-analysis. When peak height is not taken into consideration, we show that the popular ALE procedure is a good alternative in terms of the balance between type I and II errors. However, it requires more studies compared to other procedures in terms of activation reliability. Finally, we discuss the differences, interpretations, and limitations of our results.

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

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

Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies

Peak-based meta-analyses of neuroimaging studies create, for each study, a brain map of effect size or peak likelihood by convolving a kernel with each reported peak. A kernel is a small matrix applied in order that voxels surrounding the peak have a value similar to, but slightly lower than that of the peak. Current kernels are isotropic, i.e., the value of a voxel close to a peak only depends on the Euclidean distance between the voxel and the peak. However, such perfect spheres of effect size or likelihood around the peak are rather implausible: a voxel that correlates with the peak across individuals is more likely to be part of the cluster of significant activation or difference than voxels uncorrelated with the peak. This paper introduces anisotropic kernels, which assign different values to the different neighboring voxels based on the spatial correlation between them. They are specifically developed for effect-size signed differential mapping (ES-SDM), though might be easily implemented in other meta-analysis packages such as activation likelihood estimation (ALE). The paper also describes the creation of the required correlation templates for gray matter/BOLD response, white matter, cerebrospinal fluid, and fractional anisotropy. Finally, the new method is validated by quantifying the accuracy of the recreation of effect size maps from peak information. This empirical validation showed that the optimal degree of anisotropy and full-width at half-maximum (FWHM) might vary largely depending on the specific data meta-analyzed. However, it also showed that the recreation substantially improved and did not depend on the FWHM when full anisotropy was used. Based on these results, we recommend the use of fully anisotropic kernels in ES-SDM and ALE, unless optimal meta-analysis-specific parameters can be estimated based on the recreation of available statistical maps. The new method and templates are freely available at http://www.sdmproject.com/.