Automated analysis of meta-analysis networks

Maintenance and consolidation strategies for patients with untreated advanced follicular lymphoma: A systematic review and network meta-analysis of randomized trials

BACKGROUND

The emergence of novel and efficient antibody maintenance approaches has provided more options for post-induction treatment of advanced follicular lymphoma (FL), and further comparisons are required to determine the most clinically beneficial regimen. The authors conducted a systematic review and meta-analysis to evaluate the maintenance or consolidation strategy.

METHODS

The authors performed two independent searches in PubMed, Web of Science, the Cochrane library databases, Scopus, and Embase for randomized controlled trials (RCTs) evaluating maintenance or consolidation therapy in untreated FL patients. Extracted data included the clinical characteristics, treatment regimen, progression-free survival (PFS), overall survival (OS), and adverse effects. They then pooled the data and used a Bayesian random-effects model to combine direct comparisons with indirect evidence.

RESULTS

The authors screened 1515 records and identified 13 eligible RCTs that assessed nine different regimens in 5681 advanced FL patients. Reconstructed individual survival data presented that obinutuzumab had the highest effect sizes and certainty of the evidence for PFS (hazard ratio, 0.43; 95% confidence interval, 0.22-0.79) and tolerability compared with observation. However, no benefit was observed in patients according to the OS, regardless of which regimen was taken. Considering other regimens, although an extended course of rituximab maintenance and consolidation therapies presented PFS benefits compared with standard rituximab maintenance, they were also associated with higher toxicity.

CONCLUSIONS

Although obinutuzumab and rituximab maintenance treatment improved PFS significantly, its clinical benefit requires further validation in larger populations. Furthermore, because few trials informed each treatment comparison, research is needed to refine the understanding of this complex and rapidly evolving treatment landscape.

Linking neuroanatomical abnormalities in autism spectrum disorder with gene expression of candidate ASD genes: A meta-analytic and network-oriented approach

BACKGROUND

Autism spectrum disorder (ASD) is a set of developmental conditions with widespread neuroanatomical abnormalities and a strong genetic basis. Although neuroimaging studies have indicated anatomical changes in grey matter (GM) morphometry, their associations with gene expression remain elusive.

METHODS

Here, we aim to understand how gene expression correlates with neuroanatomical atypicalities in ASD. To do so, we performed a coordinate-based meta-analysis to determine the common GM variation pattern in the autistic brain. From the Allen Human Brain Atlas, we selected eight genes from the SHANK, NRXN, NLGN family and MECP2, which have been implicated with ASD, particularly in regards to altered synaptic transmission and plasticity. The gene expression maps for each gene were built. We then assessed the correlation between the gene expression maps and the GM alteration maps. Lastly, we projected the obtained clusters of GM alteration-gene correlations on top of the canonical resting state networks, in order to provide a functional characterization of the structural evidence.

RESULTS

We found that gene expression of most genes correlated with GM alteration (both increase and decrease) in regions located in the default mode network. Decreased GM was also correlated with gene expression of some ASD genes in areas associated with the dorsal attention and cerebellar network. Lastly, single genes were found to be significantly correlated with increased GM in areas located in the somatomotor, limbic and ganglia/thalamus networks.

CONCLUSIONS

This approach allowed us to combine the well beaten path of genetic and brain imaging in a novel way, to specifically investigate the relation between gene expression and brain with structural damage, and individuate genes of potential interest for further investigation in the functional domain.

Mirror neurons and empathy-related regions in psychopathy: systematic review, meta-analysis, and a working model

Mirror neurons have been associated with empathy. People with psychopathic traits present low levels of empathy. To analyze this, a systematic review of fMRI studies of people with psychopathic traits during an emotional facial expression processing task was performed. The regions of interest were structures associated with the mirror neuron system: ventromedial prefrontal cortex (vmPFC), inferior parietal lobe (IPL), inferior frontal gyrus and superior temporal sulcus. The analysis was also extended to structures related to affective empathy (insula, amygdala and anterior cingulate cortex) and to two more emotional processing areas (orbitofrontal cortex and fusiform gyrus). Hypoactivation was more frequently observed in regions of the mirror neuron system from people with high psychopathic traits, as well as in the emotional processing structures, and those associated with affective empathy, except for the insula, where it presented higher activity. Differences were observed for all types of emotions. The results suggest that the mirror neuron system is altered in psychopathy and their relationship with affective empathy deficits is discussed.

Uncovering neural distinctions and commodities between two creativity subsets: A meta-analysis of fMRI studies in divergent thinking and insight using activation likelihood estimation

The dual‐process theory that two different systems of thought coexist in creative thinking has attracted considerable attention. In the field of creative thinking, divergent thinking (DT) is the ability to produce multiple solutions to open‐ended problems in a short time. It is mainly considered an associative and fast process. Meanwhile, insight, the new and unexpected comprehension of close‐ended problems, is frequently marked as a deliberate and time‐consuming thinking process requiring concentrated effort. Previous research has been dedicated to revealing their separate neural mechanisms, while few studies have compared their differences and similarities at the brain level. Therefore, the current study applied Activation Likelihood Estimation to decipher common and distinctive neural pathways that potentially underlie DT and insight. We selected 27 DT studies and 30 insight studies for retrospective meta‐analyses. Initially, two single analyses with follow‐up contrast and conjunction analyses were performed. The single analyses showed that DT mainly involved the inferior parietal lobe (IPL), cuneus, and middle frontal gyrus (MFG), while the precentral gyrus, inferior frontal gyrus (IFG), parahippocampal gyrus (PG), amygdala (AMG), and superior parietal lobe were engaged in insight. Compared to insight, DT mainly led to greater activation in the IPL, the crucial part of the default mode network. However, insight caused more significant activation in regions related to executive control functions and emotional responses, such as the IFG, MFG, PG, and AMG. Notably, the conjunction analysis detected no overlapped areas between DT and insight. These neural findings implicate that various neurocognitive circuits may support DT and insight.

Bilateral intraparietal activation for number tasks in studies using an adaptation paradigm: A meta-analysis

Mathematical processing is important for professional successes. The Adaptation Paradigm has been widely used to study the brain underpinnings of mathematical processing. In this study, we aim at shedding light on an important component of mathematical processing, namely numerical cognition. To do so, we performed a meta-analysis using the Activation Likelihood Estimation method on studies that have employed the Adaptation Paradigm for examining numerical cognition. We found a bilateral Intraparietal Sulcus (IPS) activation in studies using both symbolic and non-symbolic stimuli formats. We also found a right lateralized brain activation for the non-symbolic condition and a left lateralized brain activation for the symbolic condition. These results imply that the Adaptation Paradigm likely targets numeric magnitude processing and confirms the potency of this paradigm to activate the Intraparietal Sulcus.

Easy to interpret coordinate based meta-analysis of neuroimaging studies: Analysis of brain coordinates (ABC)

BACKGROUND

Functional MRI and voxel-based morphometry are important in neuroscience. They are technically challenging with no globally optimal analysis method, and the multiple approaches have been shown to produce different results. It is useful to be able to meta-analyse results from such studies that tested a similar hypothesis potentially using different analysis methods. The aim is to identify replicable results and infer hypothesis specific effects. Coordinate based meta-analysis (CBMA) offers this, but the multiple algorithms can produce different results, making interpretation conditional on the algorithm.

NEW METHOD

Here a new model based CBMA algorithm, Analysis of Brain Coordinates (ABC), is presented. ABC aims to be simple to understand by avoiding empirical elements where possible and by using a simple to interpret statistical threshold, which relates to the primary aim of detecting replicable effects.

RESULTS

ABC is compared to both the most used and the most recently developed CBMA algorithms, by reproducing a published meta-analysis of localised grey matter changes in schizophrenia. There are some differences in results and the type of data that can be analysed, which are related to the algorithm specifics.

COMPARISON TO OTHER METHODS

Compared to other algorithms ABC eliminates empirical elements where possible and uses a simple to interpret statistical threshold.

CONCLUSIONS

There may be no optimal way to meta-analyse neuroimaging studies using CBMA. However, by eliminating some empirical elements and relating the statistical threshold directly to the aim of finding replicable effects, ABC makes the impact of the algorithm on any conclusion easier to understand.

Applying a Probabilistic Network Method to Solve Business-Related Few-Shot Classification Problems

It can be challenging to learn algorithms due to the research of business-related few-shot classification problems. Therefore, in this paper, we evaluate the classification of few-shot learning in the commercial field. To accurately identify the categories of few-shot learning problems, we proposed a probabilistic network (PN) method based on few-shot and one-shot learning problems. The enhancement of the original data was followed by the subsequent development of the PN method based on feature extraction, category comparison, and loss function analysis. The effectiveness of the method was validated using two examples (absenteeism at work and Las Vegas Strip hotels). Experimental results demonstrate the ability of the PN method to effectively identify the categories of commercial few-shot learning problems. Therefore, the proposed method can be applied to business-related few-shot classification problems.

Coordinate based meta-analysis of networks in neuroimaging studies

Meta-analysis of summary results from published neuroimaging studies independently testing a common hypothesis is performed using coordinate based meta-analysis (CBMA), which tests for consistent activation (in the case of functional MRI studies) of the same anatomical regions. Using just the reported coordinates it is also possible to meta-analyse coactivated regions to reveal a network-like structure of coordinate clusters (network nodes) distributed at the coactivated locations and a measure of the coactivation strength (network edges), which is determined by the presence/absence of reported activation. Here a new coordinate-based method to estimate a network of coactivations is detailed, which utilises the Z score accompanying each reported. Coordinate based meta-analysis of networks (CBMAN) assumes that if the activation pattern reported by independent studies is truly consistent, then the relative magnitude of these Z scores might also be consistent. It is hypothesised that this is detectable as Z score covariance between coactivated regions provided the within study variances are small. Advantages of using the Z scores instead of coordinates to measure coactivation strength are that censoring by the significance thresholds can be considered, and that using a continuous measure rather than a dichotomous one can increase statistical power. CBMAN uses maximum likelihood estimation to fit multivariate normal distributions to the standardised Z scores, and the covariances are considered as edges of a network of coactivated clusters (nodes). Here it is validated by numerical simulation and demonstrated on real data used previously to demonstrate CBMA. Software to perform CBMAN is freely available.

Network localization of heterogeneous neuroimaging findings

Studies of the same disease often implicate different brain regions, contributing to a perceived reproducibility crisis in neuroimaging. Here, we leverage the normative human brain connectome to test whether seemingly heterogeneous neuroimaging findings localize to connected brain networks. We use neurodegenerative disease, and specifically Alzheimer's disease, as our example as it is one of the diseases that has been studied the most using neuroimaging. First, we show that neuroimaging findings in Alzheimer's disease occur in different brain regions across different studies but localize to the same functionally connected brain network. Second, we show that neuroimaging findings across different neurodegenerative diseases (Alzheimer's disease, frontotemporal dementia, corticobasal syndrome, and progressive non-fluent aphasia) localize to different disease-specific brain networks. Finally, we show that neuroimaging findings for a specific symptom within a disease (delusions in Alzheimer's disease) localize to a symptom-specific brain network. Our results suggest that neuroimaging studies that appear poorly reproducible may identify different regions within the same connected brain network. Human connectome data can be used to link heterogeneous neuroimaging findings to common neuroanatomy, improving localization of neuropsychiatric diseases and symptoms.

Regional Volumetric Differences Based on Structural MRI in Children With Two Subtypes of ADHD and Controls

OBJECTIVE

The purpose of this study was to compare groups of children with two subtypes of ADHD and controls on selected regions using volumetric magnetic resonance imaging (MRI) measures. Children with ADHD were expected to have smaller volumes of the anterior cingulate cortex (ACC) and caudate. Parent behavioral rating measures of hyperactivity were predicted to relate to the volume of the caudate and attention with the ACC.

METHOD

There were a total of 74 children in the final sample (27 controls, 25 ADHD:Combined type [ADHD:C], 22 ADHD:Inattentive type [ADHD:I]).

RESULTS

Findings indicated that the ADHD:C group had bilaterally smaller volumes of the caudate and ACC compared with the other two groups. In addition, parent ratings of attention and hyperactivity significantly predicted the right volume of the ACC, whereas hyperactivity ratings predicted the volume of the right caudate. Analysis of the ADHD groups without the control confirmed these findings.

CONCLUSION

These findings indicate that different structures are related to the ADHD subtypes and suggest that they may be different phenotypes.

Gender differences in working memory networks: a BrainMap meta-analysis

Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigations using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks.

Neurological soft signs are not "soft" in brain structure and functional networks: evidence from ALE meta-analysis

BACKGROUND

Neurological soft signs (NSS) are associated with schizophrenia and related psychotic disorders. NSS have been conventionally considered as clinical neurological signs without localized brain regions. However, recent brain imaging studies suggest that NSS are partly localizable and may be associated with deficits in specific brain areas.

METHOD

We conducted an activation likelihood estimation meta-analysis to quantitatively review structural and functional imaging studies that evaluated the brain correlates of NSS in patients with schizophrenia and other psychotic disorders. Six structural magnetic resonance imaging (sMRI) and 15 functional magnetic resonance imaging (fMRI) studies were included.

RESULTS

The results from meta-analysis of the sMRI studies indicated that NSS were associated with atrophy of the precentral gyrus, the cerebellum, the inferior frontal gyrus, and the thalamus. The results from meta-analysis of the fMRI studies demonstrated that the NSS-related task was significantly associated with altered brain activation in the inferior frontal gyrus, bilateral putamen, the cerebellum, and the superior temporal gyrus.

CONCLUSIONS

Our findings from both sMRI and fMRI meta-analyses further support the conceptualization of NSS as a manifestation of the "cerebello-thalamo-prefrontal" brain network model of schizophrenia and related psychotic disorders.

Meta-analysis in human neuroimaging: computational modeling of large-scale databases

Spatial normalization--applying standardized coordinates as anatomical addresses within a reference space--was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology.

Amygdala recruitment in schizophrenia in response to aversive emotional material: a meta-analysis of neuroimaging studies

Emotional dysfunction has long been established as a critical clinical feature of schizophrenia. In the past decade, there has been extensive work examining the potential contribution of abnormal amygdala activation to this dysfunction in patients with schizophrenia. A number of studies have demonstrated under-recruitment of the amygdala in response to emotional stimuli, while others have shown intact recruitment of this region. To date, there have been few attempts to synthesize this literature using quantitative criteria or to use a formal meta-analytic approach to examine which variables may moderate the magnitude of between-group differences in amygdala activation in response to aversive emotional stimuli. We conducted a meta-analysis of amygdala activation in patients with schizophrenia, using a bootstrapping approach to investigate: (a) evidence for amygdala under-recruitment in schizophrenia and (b) variables that may moderate the magnitude of between-group differences in amygdala activation. We demonstrate that patients with schizophrenia show statistically significant, but modest, under-recruitment of bilateral amygdala (mean effect size = -0.20 SD). However, present findings indicate that this under-recruitment is dependent on the use of a neutral vs emotion interaction contrast and is not apparent if amygdala activation by patients and controls is evaluated in a negative emotional condition only.

Meta-analytic clustering of the insular cortex: characterizing the meta-analytic connectivity of the insula when involved in active tasks

The human insula has been parcellated on the basis of resting state functional connectivity and diffusion tensor imaging. Little is known about the organization of the insula when involved in active tasks. We explored this issue using a novel meta-analytic clustering approach. We queried the BrainMap database asking for papers involving normal subjects that recorded activations in the insular cortex, retrieving 1305 papers, involving 22,872 subjects and a total of 2957 foci. Data were analyzed with several different methodologies, some of which expressly designed for this work. We used meta-analytic connectivity modeling and meta-analytic clustering of data obtained from the BrainMap database. We performed cluster analysis to subdivide the insula in areas with homogeneous connectivity, and density analysis of the activated foci using Voronoi tessellation. Our results confirm and extend previous findings obtained investigating the resting state connectivity of the anterior-posterior and left-right insulae. They indicate, for the first time, that some blocks of the anterior insula play the role of hubs between the anterior and the posterior insulae, as confirmed by their activation in several different paradigms. This finding supports the view that the network to which the anterior insula belongs is related to saliency detection. The insulae of both sides can be parcellated in two clusters, the anterior and the posterior: the anterior is characterized by an attentional pattern of connectivity with frontal, cingulate, parietal, cerebellar and anterior insular highly connected areas, whereas the posterior is characterized by a more local connectivity pattern with connections to sensorimotor, temporal and posterior cingulate areas. This antero-posterior subdivision, better characterized on the right side, results sharper with the connectivity based clusterization than with the behavioral based clusterization. The circuits belonging to the anterior insula are very homogeneous and their blocks in multidimensional scaling of MACM-based profiles are in central position, whereas those belonging to the posterior insula, especially on the left, are located at the periphery and sparse, thus suggesting that the posterior circuits bear a more heterogeneous connectivity. The anterior cluster is mostly activated by cognition, whereas the posterior is mostly activated by interoception, perception and emotion.

Distributed processing; distributed functions?

After more than twenty years busily mapping the human brain, what have we learned from neuroimaging? This review (coda) considers this question from the point of view of structure-function relationships and the two cornerstones of functional neuroimaging; functional segregation and integration. Despite remarkable advances and insights into the brain's functional architecture, the earliest and simplest challenge in human brain mapping remains unresolved: We do not have a principled way to map brain function onto its structure in a way that speaks directly to cognitive neuroscience. Having said this, there are distinct clues about how this might be done: First, there is a growing appreciation of the role of functional integration in the distributed nature of neuronal processing. Second, there is an emerging interest in data-driven cognitive ontologies, i.e., that are internally consistent with functional anatomy. We will focus this review on the growing momentum in the fields of functional connectivity and distributed brain responses and consider this in the light of meta-analyses that use very large data sets to disclose large-scale structure-function mappings in the human brain.

Cingulate, frontal, and parietal cortical dysfunction in attention-deficit/hyperactivity disorder

Functional and structural neuroimaging have identified abnormalities of the brain that are likely to contribute to the neuropathophysiology of attention-deficit/hyperactivity disorder (ADHD). In particular, hypofunction of the brain regions comprising the cingulo-frontal-parietal cognitive-attention network have been consistently observed across studies. These are major components of neural systems that are relevant to ADHD, including cognitive/attention networks, motor systems, and reward/feedback-based processing systems. Moreover, these areas interact with other brain circuits that have been implicated in ADHD, such as the "default mode" resting state network. The ADHD imaging data related to cingulo-frontal-parietal network dysfunction will be selectively highlighted here to help facilitate its integration with the other information presented in this special issue. Together, these reviews will help shed light on the neurobiology of ADHD.

Different functions in the cingulate cortex, a meta-analytic connectivity modeling study

The cingulate cortex is a structurally heterogeneous brain region involved in emotional, cognitive and motor tasks. With the aim of identifying which behavioral domains are associated with the activation of the cingulate cortex, we performed a structure based-meta-analysis using the activation likelihood estimation (ALE), which assesses statistical significant convergence of neuroimaging studies using the BrainMap database. To map the meta-analytic coactivation maps of the cingulate cortex (MACM), we subdivided the parenchyma along the rostro-caudal axis in 12 bilateral equispaced ROIs. ROIs were not chosen according to previously suggested subdivisions, as to obtain a completely data-driven result. Studies were included with one or more activation coordinates in at least one of the 12 pre-defined ROIs. The meta-analytic connectivity profile and behavioral domains profiles were identified for each ROI. Cluster analysis was then performed on the MACM and behavioral domains to group together ROIs with similar profiles. The results showed that the cingulate cortex can be divided in three clusters according to the MACM parcellation and in four according to the behavioral domain-based parcellation. In addition, a behavioral-domain based meta-analysis was conducted and the spatial consistency of functional connectivity patterns across different domain-related ALE results was evaluated by computing probabilistic maps. These maps identified some portions of the cingulate cortex as involved in several tasks. Our results showed the existence of a more specific functional characterization of some portions of the cingulate cortex but also a great multifunctionality of others. By analyzing a large number of studies, structure based meta-analysis can greatly contribute to new insights in the functional significance of brain activations and in the role of specific brain areas in behavior.

Functional connectivity and coactivation of the nucleus accumbens: a combined functional connectivity and structure-based meta-analysis

This article investigates the functional connectivity patterns of the nucleus accumbens (NAcc) in 18 healthy participants using a resting state functional connectivity (rsFC) protocol. Also, a meta-analytic connectivity modeling (MACM) was used to characterize patterns of functional coactivations involving NAcc: The results of a structure-based meta-analyses of 57 fMRI and PET studies were submitted to activation likelihood estimation analysis to estimate consistent activation patterns across the different imaging studies. The results of the combined rsFC and MACM analyses show that spontaneous activity in NAcc predicts activity in regions implicated in reward circuitries, including orbitomedial prefrontal cortex, globus pallidus, thalamus, midbrain, amygdala, and insula. This confirms the key role of NAcc in the mesocorticolimbic system, which integrates inputs from limbic and cortical regions. We also detected activity in brain regions having few or no direct anatomical connections with NAcc, such as sensorimotor cortex, cerebellum, medial and posterior parietal cortex, and medial/inferior temporal cortex, supporting the view that not all functional connections can be explained by anatomical connections but can also result from connections mediated by third areas. Our rsFC findings are in line with the results of the structure-based meta-analysis: MACM maps are superimposable with NAcc rsFC results, and the reward paradigm class is the one that most frequently generates activation in NAcc. Our results overlap considerably with recently proposed schemata of the main neuron systems in the limbic forebrain and in the anterior part of the limbic midbrain in rodents and nonhuman primates.

Facial emotion processing in schizophrenia: a meta-analysis of functional neuroimaging data

BACKGROUND

People with schizophrenia have difficulty with emotion perception. Functional imaging studies indicate regional brain activation abnormalities in patients with schizophrenia when processing facial emotion. However, findings have not been entirely consistent across different studies.

METHODS

Activation likelihood estimation (ALE) meta-analyses were conducted to examine brain activation during facial emotion processing in patients with schizophrenia, controls, and patients compared with controls. Secondary meta-analyses were performed to assess the contribution of task design and illness chronicity to the results reported.

RESULTS

When processing facial expressions of emotions, both patients with schizophrenia and healthy controls activated the bilateral amygdala and right fusiform gyri. However, the extent of activation in these regions was generally much more limited in the schizophrenia samples. When directly compared with controls, the extent of activation in bilateral amygdala, parahippocampal gyrus and fusiform gyrus, right superior frontal gyrus, and lentiform nucleus was significantly less in patients. Patients with schizophrenia, but not controls, activated the left insula. A relative failure to recruit the amygdala in patients occurred regardless of whether the task design was explicit or implicit, while differences in fusiform activation were evident in explicit, not implicit, tasks. Restricting the analysis to patients with chronic illness did not substantially change the results.

CONCLUSIONS

A marked underrecruitment of the amygdala, accompanied by a substantial limitation in activation throughout a ventral temporal-basal ganglia-prefrontal cortex "social brain" system may be central to the difficulties patients experience when processing facial emotion.

Attention-deficit/hyperactivity disorder and attention networks

Research attempting to elucidate the neuropathophysiology of attention-deficit/hyperactivity disorder (ADHD) has not only shed light on the disorder itself, it has simultaneously provided new insights into the mechanisms of normal cognition and attention. This review will highlight and integrate this bidirectional flow of information. Following a brief overview of ADHD clinical phenomenology, ADHD studies will be placed into a wider historical perspective by providing illustrative examples of how major models of attention have influenced the development of neurocircuitry models of ADHD. The review will then identify major components of neural systems potentially relevant to ADHD, including attention networks, reward/feedback-based processing systems, as well as a 'default mode' resting state network. Further, it will suggest ways in which these systems may interact and be influenced by neuromodulatory factors. Recent ADHD imaging data will be selectively provided to both illustrate the field's current level of knowledge and to show how such data can inform our understanding of normal brain functions. The review will conclude by suggesting possible avenues for future research.

Learning partially directed functional networks from meta-analysis imaging data

We propose a new exploratory method for the discovery of partially directed functional networks from fMRI meta-analysis data. The method performs structure learning of Bayesian networks in search of directed probabilistic dependencies between brain regions. Learning is based on the co-activation of brain regions observed across several independent imaging experiments. In a series of simulations, we first demonstrate the reliability of the method. We then present the application of our approach in an extensive meta-analysis including several thousand activation coordinates from more than 500 imaging studies. Results show that our method is able to automatically infer Bayesian networks that capture both directed and undirected probabilistic dependencies between a number of brain regions, including regions that are frequently observed in motor-related and cognitive control tasks.

The restless brain: attention-deficit hyperactivity disorder, resting-state functional connectivity, and intrasubject variability

OBJECTIVES

To highlight recent advances in the conceptualization of attention-deficit hyperactivity disorder (ADHD) emerging from neuroimaging and endophenotypic approaches.

METHODS

We selectively reviewed recent published literature on the phenomena of resting-state functional connectivity, intrasubject variability, and diffusion tensor imaging pertaining to ADHD.

RESULTS

Recent advances based on the novel approach of resting-state functional connectivity appear to be highly promising and likely to link to studies of intrasubject variability.

CONCLUSIONS

Endophenotypic fractionation may offer a means of addressing the complex heterogeneity of ADHD on the path to testable models of pathophysiology. Such models focusing on intrasubject variability, intrinsic brain activity, and reward-related processing are progressing rapidly.

Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia

CONTEXT

Prefrontal cortical dysfunction is frequently reported in schizophrenia. It remains unclear whether this represents the coincidence of several prefrontal region- and process-specific impairments or a more unitary dysfunction in a superordinate cognitive control network. Whether these impairments are properly considered reflective of hypofrontality vs hyperfrontality remains unresolved.

OBJECTIVES

To test whether common nodes of the cognitive control network exhibit altered activity across functional neuroimaging studies of executive cognition in schizophrenia and to evaluate the direction of these effects.

DATA SOURCES

PubMed database.

STUDY SELECTION

Forty-one English-language, peer-reviewed articles published prior to February 2007 were included. All reports used functional neuroimaging during executive function performance by adult patients with schizophrenia and reported whole-brain analyses in standard stereotactic space. Tasks primarily included the delayed match-to-sample, N-back, AX-CPT, and Stroop tasks.

DATA EXTRACTION

Activation likelihood estimation modeling reported activation maxima as the center of a 3-dimensional gaussian function in the meta-analysis, with statistical thresholding and correction for multiple comparisons.

DATA SYNTHESIS

In within-group analyses, healthy controls and patients activated a similarly distributed cortical-subcortical network, prominently including the dorsolateral prefrontal cortex (PFC), ventrolateral PFC, anterior cingulate cortex (ACC), and thalamus. In between-group analyses, patients showed reduced activation in the left dorsolateral PFC, rostral/dorsal ACC, left thalamus (with significant co-occurrence of these areas), and inferior/posterior cortical areas. Increased activation was observed in several midline cortical areas. Activation within groups varied modestly by task.

CONCLUSIONS

Healthy adults and schizophrenic patients activate a qualitatively similar neural network during executive task performance, consistent with the engagement of a general-purpose cognitive control network, with critical nodes in the dorsolateral PFC and ACC. Nevertheless, patients with schizophrenia show altered activity with deficits in the dorsolateral PFC, ACC, and mediodorsal nucleus of the thalamus. Increases in activity are evident in other PFC areas, which could be compensatory in nature.

ALE Meta-Analysis Workflows Via the Brainmap Database: Progress Towards A Probabilistic Functional Brain Atlas

With the ever-increasing number of studies in human functional brain mapping, an abundance of data has been generated that is ready to be synthesized and modeled on a large scale. The BrainMap database archives peak coordinates from published neuroimaging studies, along with the corresponding metadata that summarize the experimental design. BrainMap was designed to facilitate quantitative meta-analysis of neuroimaging results reported in the literature and supports the use of the activation likelihood estimation (ALE) method. In this paper, we present a discussion of the potential analyses that are possible using the BrainMap database and coordinate-based ALE meta-analyses, along with some examples of how these tools can be applied to create a probabilistic atlas and ontological system of describing function–structure correspondences.

Functional brain correlates of social and nonsocial processes in autism spectrum disorders: an activation likelihood estimation meta-analysis

BACKGROUND

Functional neuroimaging studies of autism spectrum disorders (ASD) have examined social and nonsocial paradigms, although rarely in the same study. Here, we provide an objective, unbiased survey of functional brain abnormalities in ASD, related to both social and nonsocial processing.

METHODS

We conducted two separate voxel-wise activation likelihood estimation meta-analyses of 39 functional neuroimaging studies consisting of 24 studies examining social processes (e.g., theory of mind, face perception) and 15 studies examining nonsocial processes (e.g., attention control, working memory). Voxel-wise significance threshold was p<.05, corrected by false discovery rate.

RESULTS

Compared with neurotypical control (NC) subjects, ASD showed greater likelihood of hypoactivation in two medial wall regions: perigenual anterior cingulate cortex (ACC) in social tasks only and dorsal ACC in nonsocial studies. Further, right anterior insula, recently linked to social cognition, was more likely to be hypoactivated in ASD in the analyses of social studies. In nonsocial studies, group comparisons showed greater likelihood of activation for the ASD group in the rostral ACC region that is typically suppressed during attentionally demanding tasks.

CONCLUSIONS

Despite substantial heterogeneity of tasks, the rapidly increasing functional imaging literature showed ASD-related patterns of hypofunction and aberrant activation that depended on the specific cognitive domain, i.e., social versus nonsocial. These results provide a basis for targeted extensions of these findings with younger subjects and a range of paradigms, including analyses of default mode network regulation in ASD.

Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis

BACKGROUND

Although structural neuroimaging methods have been widely used to study brain morphology in schizophrenia, synthesizing this literature has been difficult. With the increasing popularity of voxel-based morphometric (VBM) methods in which group differences are reported in standardized coordinates, it is possible to apply powerful meta-analytic techniques initially designed for functional neuroimaging. In this study, we performed a voxelwise, coordinate-based meta-analysis to better conceptualize the neuroanatomic correlates of schizophrenia.

METHODS

Thirty-one peer-reviewed articles, with a total of 1195 patients with schizophrenia contrasted with 1262 healthy volunteers, were included in the meta-analysis. Coordinates from each article were used to create a statistical map that estimated the likelihood of between-group gray matter density differences at every brain voxel. These results were subsequently entered into a network analysis.

RESULTS

Patients had reduced gray matter density relative to control subjects in a distributed network of regions, including bilateral insular cortex, anterior cingulate, left parahippocampal gyrus, left middle frontal gyrus, postcentral gyrus, and thalamus. Network analysis grouped these regions into four distinct networks that potentially represent different pathologic processes. Patients had increased gray matter density in striatal regions.

CONCLUSIONS

This study expands on previous meta-analyses of the neuroanatomy of schizophrenia by elucidating a series of brain networks disrupted by the illness. Because it is possible that these networks are influenced by independent etiologic factors, this work should foster more detailed neural models of the illness and focus research designed to discover the mechanisms of gray matter reduction in schizophrenia.

The extended language network: a meta-analysis of neuroimaging studies on text comprehension

Language processing in context requires more than merely comprehending words and sentences. Important subprocesses are inferences for bridging successive utterances, the use of background knowledge and discourse context, and pragmatic interpretations. The functional neuroanatomy of these text comprehension processes has only recently been investigated. Although there is evidence for right-hemisphere contributions, reviews have implicated the left lateral prefrontal cortex, left temporal regions beyond Wernicke's area, and the left dorso-medial prefrontal cortex (dmPFC) for text comprehension. To objectively confirm this extended language network and to evaluate the respective contribution of right hemisphere regions, meta-analyses of 23 neuroimaging studies are reported here. The analyses used replicator dynamics based on activation likelihood estimates. Independent of the baseline, the anterior temporal lobes (aTL) were active bilaterally. In addition, processing of coherent compared with incoherent text engaged the dmPFC and the posterior cingulate cortex. Right hemisphere activations were seen most notably in the analysis of contrasts testing specific subprocesses, such as metaphor comprehension. These results suggest task dependent contributions for the lateral PFC and the right hemisphere. Most importantly, they confirm the role of the aTL and the fronto-medial cortex for language processing in context.

Model-based clustering of meta-analytic functional imaging data

We present a method for the analysis of meta-analytic functional imaging data. It is based on Activation Likelihood Estimation (ALE) and subsequent model-based clustering using Gaussian mixture models, expectation-maximization (EM) for model fitting, and the Bayesian Information Criterion (BIC) for model selection. Our method facilitates the clustering of activation maxima from previously performed imaging experiments in a hierarchical fashion. Regions with a high concentration of activation coordinates are first identified using ALE. Activation coordinates within these regions are then subjected to model-based clustering for a more detailed cluster analysis. We demonstrate the usefulness of the method in a meta-analysis of 26 fMRI studies investigating the well-known Stroop paradigm.

Functional coactivation map of the human brain

Understanding the interactions among different brain regions is fundamental to our understanding of brain function. Here we describe a complete map of functional connections in the human brain derived by an automatic meta-analysis of 825 neuroimaging articles, representing 3402 experiments. The likelihood of a functional connection between regions was estimated by studying the interdependence of their "activity," as reported in each experiment, across all experiments. We obtained a dense coactivation map that recovers some fundamental principles of the brain's functional connectivity, such as the symmetric interhemispheric connections, and important functional networks, such as the fronto-parietal attention network, the resting state network and the motor network.

The neural correlates of attention deficit hyperactivity disorder: an ALE meta-analysis

BACKGROUND

Attention deficit/hyperactivity disorder (ADHD) is one of the most prevalent and commonly studied forms of psychopathology in children and adolescents. Causal models of ADHD have long implicated dysfunction in fronto-striatal and frontal-parietal networks supporting executive function, a hypothesis that can now be examined systematically using functional neuroimaging. The present work provides an objective, unbiased statistically-based meta-analysis of published functional neuroimaging studies of ADHD.

METHODS

A recently developed voxel-wise quantitative meta-analytic technique known as activation likelihood estimation (ALE) was applied to 16 neuroimaging studies examining and contrasting patterns of neural activity in patients with ADHD and healthy controls. Voxel-wise results are reported using a statistical threshold of p < .05, corrected. Given the large number of studies examining response inhibition, additional meta-analyses focusing specifically on group differences in the neural correlates of inhibition were included.

RESULTS

Across studies, significant patterns of frontal hypoactivity were detected in patients with ADHD, affecting anterior cingulate, dorsolateral prefrontal, and inferior prefrontal cortices, as well as related regions including basal ganglia, thalamus, and portions of parietal cortex. When focusing on studies of response inhibition alone, a more limited set of group differences were observed, including inferior prefrontal cortex, medial wall regions, and the precentral gyrus. In contrast, analyses focusing on studies of constructs other than response inhibition revealed a more extensive pattern of hypofunction in patients with ADHD than those of response inhibition.

CONCLUSIONS

To date, the most consistent findings in the neuroimaging literature of ADHD are deficits in neural activity within fronto-striatal and fronto-parietal circuits. The distributed nature of these results fails to support models emphasizing dysfunction in any one frontal sub-region. While our findings are suggestive of the primacy of deficits in frontal-based neural circuitry underlying ADHD, we discuss potential biases in the literature that need to be addressed before such a conclusion can be fully embraced.

Stuttered and fluent speech production: an ALE meta-analysis of functional neuroimaging studies

This study reports an activation likelihood estimation (ALE) meta-analysis of imaging studies of chronic developmental stuttering in adults. Two parallel meta-analyses were carried out: (1) stuttered production in the stutterers; (2) fluent production in the control subjects. The control subjects' data replicated previous analyses of single-word reading, identifying activation in primary motor cortex, premotor cortex, supplementary motor area, Rolandic operculum, lateral cerebellum, and auditory areas, among others. The stuttering subjects' analysis showed that similar brain areas are involved in stuttered speech as in fluent speech, but with some important differences. Motor areas were over-activated in stuttering, including primary motor cortex, supplementary motor area, cingulate motor area, and cerebellar vermis. Frontal operculum, Rolandic operculum, and anterior insula showed anomalous right-laterality in stutterers. Auditory activations, due to hearing one's own speech, were essentially undetectable in stutterers. The phenomenon of efference copy is proposed as a unifying account of the pattern activation revealed within this ALE meta-analysis. This provides the basis for a stuttering system model that is testable and should help to advance the understanding and treatment of this disorder.

BrainMap taxonomy of experimental design: description and evaluation

Coordinate-based, voxel-wise meta-analysis is an exciting recent addition to the human functional brain mapping literature. In view of the critical importance of selection criteria for any valid meta-analysis, a taxonomy of experimental design should be an important tool for aiding in the design of rigorous meta-analyses. The coding scheme of experimental designs developed for and implemented within the BrainMap database provides a candidate taxonomy. In this study, the BrainMap experimental-design taxonomy is described and evaluated by comparing taxonomy fields to data-filtering choices made by subject-matter experts carrying out meta-analyses of the functional imaging literature. Fifteen publications reporting a total of 46 voxel-wise meta-analyses were included in this assessment. Collectively these 46 meta-analyses pooled data from 351 publications, selected for experimental similarity within each meta-analysis. Filter implementations within BrainMap were graded by ease-of-use (A-C) and by stage-of-use (1-3). Quality filters and content filters were tabulated separately. Quality filters required for data entry into BrainMap were classed as mandatory (five filters), being above the use grading system. All authors spontaneously adopted the five mandatory filters in constructing their meta-analysis, indicating excellent agreement on data quality among authors and between authors and the BrainMap development team. Two non-mandatory quality filters (group size and imaging modality) were applied by all authors; both were Stage 1, Grade A filters. Field-of-view filters were the least-accessible quality filters (Stage 3, Grade C); two field-of-view filters were applied by six and four authors, respectively. Authors made a total of 115 content-filter choices. Of these, 78 (68%) were Stage 1, Grade A filters; 16 (14%) were Stage 2, Grade A; and 21 (18%) were Stage 2, Grade C. No author-applied filter was absent from the taxonomy.