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

The Human Brain Traverses a Common Activation-Pattern State Space Across Task and Rest

Much of our lives are spent in unconstrained rest states, yet cognitive brain processes are primarily investigated using task-constrained states. It may be possible to utilize the insights gained from experimental control of task processes as reference points for investigating unconstrained rest. To facilitate comparison of rest and task functional magnetic resonance imaging data, we focused on activation amplitude patterns, commonly used for task but not rest analyses. During rest, we identified spontaneous changes in temporally extended whole-brain activation-pattern states. This revealed a hierarchical organization of rest states. The top consisted of two competing states consistent with previously identified "task-positive" and "task-negative" activation patterns. These states were composed of more specific states that repeated over time and across individuals. Contrasting with the view that rest consists of only task-negative states, task-positive states occurred 40% of the time while individuals "rested," suggesting task-focused activity may occur during rest. Together our results suggest that brain activation dynamics form a general hierarchy across task and rest, with a small number of dominant general states reflecting basic functional modes and a variety of specific states potentially reflecting a wide variety of cognitive processes.

Transcranial brain atlas

We introduce here the concept of a transcranial brain atlas (TBA), a new kind of brain atlas specialized for transcranial techniques. A TBA is a probabilistic mapping from scalp space to atlas label space, relating scalp locations to anatomical, functional, network, genetic, or other labels. TBAs offer a new way to integrate and present structural and functional organization of the brain and allow previously subsurface and invisible atlas labels visible on the scalp surface to accurately guide the placement of transcranial devices directly on the scalp surface in a straightforward, visual manner. We present here a framework for building TBAs that includes (i) a new, continuous proportional coordinate system devised for the scalp surface to allow standardized specification of scalp positions; (ii) a high-resolution, large sample-based (114-participant) mapping from scalp space to brain space to accurately and reliably describe human cranio-cortical correspondence; and (iii) a two-step Markov chain to combine the probabilistic scalp-brain mapping with a traditional brain atlas, bringing atlas labels to the scalp surface. We assessed the reproducibility (consistency of TBAs generated from different groups) and predictiveness (prediction accuracy of labels for individuals without brain images) of the TBAs built via our framework. Moreover, we present an application of TBAs to a functional near-infrared spectroscopy finger-tapping experiment, illustrating the utility and benefits of TBAs in transcranial studies. Our results demonstrate that TBAs can support ongoing efforts to map the human brain using transcranial techniques, just as traditional brain atlases have supported magnetic resonance imaging and positron emission tomography studies.

Neural mapping of guilt: a quantitative meta-analysis of functional imaging studies

Guilt is a self-conscious emotion associated with the negative appraisal of one's behavior. In recent years, several neuroimaging studies have investigated the neural correlates of guilt, but no meta-analyses have yet identified the most robust activation patterns. A systematic review of literature found 16 functional magnetic resonance imaging studies with whole-brain analyses meeting the inclusion criteria, for a total of 325 participants and 135 foci of activation. A meta-analysis was then conducted using activation likelihood estimation. Additionally, Meta-Analytic Connectivity Modeling (MACM) analysis was conducted to investigate the functional connectivity of significant clusters. The analysis revealed 12 significant clusters of brain activation (voxel-based FDR-corrected p < 0.05) located in the prefrontal, temporal and parietal regions, mainly in the left hemisphere. Only the left dorsal cingulate cluster survived stringent FWE correction (voxel-based p < 0.05). Secondary analyses (voxel-based FDR-corrected p < 0.05) on the 7 studies contrasting guilt with another emotional condition showed an association with clusters in the left precuneus, the anterior cingulate, the left medial frontal gyrus, the right superior frontal gyrus and the left superior temporal gyrus. MACM demonstrated that regions associated with guilt are highly interconnected. Our analysis identified a distributed neural network of left-lateralized regions associated with guilt. While voxel-based FDR-corrected results should be considered exploratory, the dorsal cingulate was robustly associated with guilt. We speculate that this network integrates cognitive and emotional processes involved in the experience of guilt, including self-representation, theory of mind, conflict monitoring and moral values. Limitations of our meta-analyses comprise the small sample size and the heterogeneity of included studies, and concerns about naturalistic validity.

A domain-general brain network underlying emotional and cognitive interference processing: evidence from coordinate-based and functional connectivity meta-analyses

The inability to control or inhibit emotional distractors characterizes a range of psychiatric disorders. Despite the use of a variety of task paradigms to determine the mechanisms underlying the control of emotional interference, a precise characterization of the brain regions and networks that support emotional interference processing remains elusive. Here, we performed coordinate-based and functional connectivity meta-analyses to determine the brain networks underlying emotional interference. Paradigms addressing interference processing in the cognitive or emotional domain were included in the meta-analyses, particularly the Stroop, Flanker, and Simon tasks. Our results revealed a consistent involvement of the bilateral dorsal anterior cingulate cortex, anterior insula, left inferior frontal gyrus, and superior parietal lobule during emotional interference. Follow-up conjunction analyses identified correspondence in these regions between emotional and cognitive interference processing. Finally, the patterns of functional connectivity of these regions were examined using resting-state functional connectivity and meta-analytic connectivity modeling. These regions were strongly connected as a distributed system, primarily mapping onto fronto-parietal control, ventral attention, and dorsal attention networks. Together, the present findings indicate that a domain-general neural system is engaged across multiple types of interference processing and that regulating emotional and cognitive interference depends on interactions between large-scale distributed brain networks.

Differences in Cortical Structure and Functional MRI Connectivity in High Functioning Autism

Autism spectrum disorders (ASD) represent a complex group of neurodevelopmental conditions characterized by deficits in communication and social behaviors. We examined the functional connectivity (FC) of the default mode network (DMN) and its relation to multimodal morphometry to investigate superregional, system-level alterations in a group of 22 adolescents and young adults with high-functioning autism compared to age-, and intelligence quotient-matched 29 healthy controls. The main findings were that ASD patients had gray matter (GM) reduction, decreased cortical thickness and larger cortical surface areas in several brain regions, including the cingulate, temporal lobes, and amygdala, as well as increased gyrification in regions associated with encoding visual memories and areas of the sensorimotor component of the DMN, more pronounced in the left hemisphere. Moreover, patients with ASD had decreased connectivity between the posterior cingulate cortex, and areas of the executive control component of the DMN and increased FC between the anteromedial prefrontal cortex and areas of the sensorimotor component of the DMN. Reduced cortical thickness in the right inferior frontal lobe correlated with higher social impairment according to the scores of the Autism Diagnostic Interview-Revised (ADI-R). Reduced cortical thickness in left frontal regions, as well as an increased cortical thickness in the right temporal pole and posterior cingulate, were associated with worse scores on the communication domain of the ADI-R. We found no association between scores on the restrictive and repetitive behaviors domain of ADI-R with structural measures or FC. The combination of these structural and connectivity abnormalities may help to explain some of the core behaviors in high-functioning ASD and need to be investigated further.

Dissociations of cognitive inhibition, response inhibition, and emotional interference: Voxelwise ALE meta-analyses of fMRI studies

Inhibitory control is the stopping of a mental process with or without intention, conceptualized as mental suppression of competing information because of limited cognitive capacity. Inhibitory control dysfunction is a core characteristic of many major psychiatric disorders. Inhibition is generally thought to involve the prefrontal cortex; however, a single inhibitory mechanism is insufficient for interpreting the heterogeneous nature of human cognition. It remains unclear whether different dimensions of inhibitory processes-specifically cognitive inhibition, response inhibition, and emotional interference-rely on dissociated neural systems. We conducted systematic meta-analyses of fMRI studies in the BrainMap database supplemented by PubMed using whole-brain activation likelihood estimation. A total of 66 study experiments including 1,447 participants and 987 foci revealed that while the left anterior insula was concordant in all inhibitory dimensions, cognitive inhibition reliably activated specific dorsal frontal inhibitory system, engaging dorsal anterior cingulate, dorsolateral prefrontal cortex, and parietal areas, whereas emotional interference reliably implicated a ventral inhibitory system, involving the ventral surface of the inferior frontal gyrus and the amygdala. Response inhibition showed concordant clusters in the fronto-striatal system, including the dorsal anterior cingulate region and extended supplementary motor areas, the dorsal and ventral lateral prefrontal cortex, basal ganglia, midbrain regions, and parietal regions. We provide an empirically derived dimensional model of inhibition characterizing neural systems underlying different aspects of inhibitory mechanisms. This study offers a fundamental framework to advance current understanding of inhibition and provides new insights for future clinical research into disorders with different types of inhibition-related dysfunctions.

Convergence Analysis of Micro-Lesions (CAML): An approach to mapping of diffuse lesions from carotid revascularization

Carotid revascularization (endarterectomy, stenting) prevents stroke; however, procedure-related embolization is common and results in small brain lesions easily identified by diffusion weighted magnetic resonance imaging (DWI). A crucial barrier to understanding the clinical significance of these lesions has been the lack of a statistical approach to identify vulnerable brain areas. The problem is that the lesions are small, numerous, and non-overlapping. Here we address this problem with a new method, the Convergence Analysis of Micro-Lesions (CAML) technique, an extension of the Anatomic Likelihood Analysis (ALE). The method combines manual lesion tracing, constraints based on known lesion patterns, and convergence analysis to represent regions vulnerable to lesions as probabilistic brain atlases. Two studies were conducted over the course of 12 years in an active, vascular surgery clinic. An analysis in an initial group of 126 patients at 1.5 T MRI was cross-validated in a second group of 80 patients at 3T MRI. In CAML, lesions were manually defined and center points identified. Brains were aligned according to side of surgery since this factor powerfully determines lesion distribution. A convergence based analysis, was performed on each of these groups. Results indicated the most consistent region of vulnerability was in motor and premotor cortex regions. Smaller regions common to both groups included the dorsolateral prefrontal cortex and medial parietal regions. Vulnerability of motor cortex is consistent with previous work showing changes in hand dexterity associated with these procedures. The consistency of CAML also demonstrates the feasibility of this new approach to characterize small, diffuse, non-overlapping lesions in patients with multifocal pathologies.

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Convergence Analysis of Micro-Lesions technique finds patterns in diffuse lesions.

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Lesions from carotid revascularization affect consistent brain targets.

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Motor cortex is the most vulnerable brain region to these lesions.

Correlations Between Personality and Brain Structure: A Crucial Role of Gender

Previous studies have shown that males and females differ in personality and gender differences have also been reported in brain structure. However, effects of gender on this "personality-brain" relationship are yet unknown. We therefore investigated if the neural correlates of personality differ between males and females. Whole brain voxel-based morphometry was used to investigate the influence of gender on associations between NEO FFI personality traits and gray matter volume (GMV) in a matched sample of 182 males and 182 females. In order to assess associations independent of and dependent on gender, personality-GMV relationships were tested across the entire sample and separately for males and females. There were no significant correlations between any personality scale and GMV in the analyses across the entire sample. In contrast, significant associations with GMV were detected for neuroticism, extraversion, and conscientiousness only in males. Interestingly, GMV in left precuneus/parieto-occipital sulcus correlated with all 3 traits. Thus, our results indicate that brain structure-personality relationships are highly dependent on gender, which might be attributable to hormonal interplays or differences in brain organization between males and females. Our results thus provide possible neural substrates of personality-behavior relationships and underline the important role of gender in these associations.

Towards a human self-regulation system: Common and distinct neural signatures of emotional and behavioural control

Self-regulation refers to controlling our emotions and actions in the pursuit of higher-order goals. Although research suggests commonalities in the cognitive control of emotion and action, evidence for a shared neural substrate is scant and largely circumstantial. Here we report on two large-scale meta-analyses of human neuroimaging studies on emotion or action control, yielding two fronto-parieto-insular networks. The networks’ overlap, however, was restricted to four brain regions: posteromedial prefrontal cortex, bilateral anterior insula, and right temporo-parietal junction. Conversely, meta-analytic contrasts revealed major between-network differences, which were independently corroborated by clustering domain-specific regions based on their intrinsic functional connectivity, as well as by functionally characterizing network sub-clusters using the BrainMap database for quantitative forward and reverse inference. Collectively, our analyses identified a core system for implementing self-control across emotion and action, beyond which, however, either regulation facet appears to rely on broadly similar yet distinct subnetworks. These insights into the neurocircuitry subserving affective and executive facets of self-control suggest both processing commonalities and differences between the two aspects of human self-regulation.

The morphometric co-atrophy networking of schizophrenia, autistic and obsessive spectrum disorders

By means of a novel methodology that can statistically derive patterns of co-alterations distribution from voxel-based morphological data, this study analyzes the patterns of brain alterations of three important psychiatric spectra-that is, schizophrenia spectrum disorder (SCZD), autistic spectrum disorder (ASD), and obsessive-compulsive spectrum disorder (OCSD). Our analysis provides five important results. First, in SCZD, ASD, and OCSD brain alterations do not distribute randomly but, rather, follow network-like patterns of co-alteration. Second, the clusters of co-altered areas form a net of alterations that can be defined as morphometric co-alteration network or co-atrophy network (in the case of gray matter decreases). Third, within this network certain cerebral areas can be identified as pathoconnectivity hubs, the alteration of which is supposed to enhance the development of neuronal abnormalities. Fourth, within the morphometric co-atrophy network of SCZD, ASD, and OCSD, a subnetwork composed of eleven highly connected nodes can be distinguished. This subnetwork encompasses the anterior insulae, inferior frontal areas, left superior temporal areas, left parahippocampal regions, left thalamus and right precentral gyri. Fifth, the co-altered areas also exhibit a normal structural covariance pattern which overlaps, for some of these areas (like the insulae), the co-alteration pattern. These findings reveal that, similarly to neurodegenerative diseases, psychiatric disorders are characterized by anatomical alterations that distribute according to connectivity constraints so as to form identifiable morphometric co-atrophy patterns.

Meta-analytic evidence for altered mesolimbic responses to reward in schizophrenia

Dysfunction of reward-related neural circuitry in schizophrenia (SCZ) has been widely reported, and may provide insight into the motivational and cognitive disturbances that characterize the disorder. Although previous meta-analyses of reward learning paradigms in SCZ have been performed, a meta-analysis of whole-brain coordinate maps in SCZ alone has not been conducted. In this study, we performed an activation likelihood estimate (ALE) meta-analysis, and performed a follow-up analysis of functional connectivity and functional decoding of identified regions. We report several salient findings that extend prior work in this area. First, an alteration in reward-related activation was observed in the right ventral striatum, but this was not solely driven by hypoactivation in the SCZ group compared to healthy controls. Second, the region was characterized by functional connectivity primarily with the lateral prefrontal cortex and pre-supplementary motor area (preSMA), as well as subcortical regions such as the thalamus which show structural deficits in SCZ. Finally, although the meta-analysis showed no regions outside the ventral striatum to be significantly altered, regions with higher functional connectivity with the ventral striatum showed a greater number of subthreshold foci. Together, these findings confirm the alteration of ventral striatal function in SCZ, but suggest that a network-based approach may assist future analysis of the functional underpinnings of the disorder.

Neuroimaging meta-analysis of cannabis use studies reveals convergent functional alterations in brain regions supporting cognitive control and reward processing

Lagging behind rapid changes to state laws, societal views, and medical practice is the scientific investigation of cannabis's impact on the human brain. While several brain imaging studies have contributed important insight into neurobiological alterations linked with cannabis use, our understanding remains limited. Here, we sought to delineate those brain regions that consistently demonstrate functional alterations among cannabis users versus non-users across neuroimaging studies using the activation likelihood estimation meta-analysis framework. In ancillary analyses, we characterized task-related brain networks that co-activate with cannabis-affected regions using data archived in a large neuroimaging repository, and then determined which psychological processes may be disrupted via functional decoding techniques. When considering convergent alterations among users, decreased activation was observed in the anterior cingulate cortex, which co-activated with frontal, parietal, and limbic areas and was linked with cognitive control processes. Similarly, decreased activation was observed in the dorsolateral prefrontal cortex, which co-activated with frontal and occipital areas and linked with attention-related processes. Conversely, increased activation among users was observed in the striatum, which co-activated with frontal, parietal, and other limbic areas and linked with reward processing. These meta-analytic outcomes indicate that cannabis use is linked with differential, region-specific effects across the brain.

Dissociable meta-analytic brain networks contribute to coordinated emotional processing

Meta-analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional- towards more network-based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta-analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta-analytic groupings of experiments demonstrating whole-brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta-analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta-analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large-scale brain networks.

The Pathoconnectivity Profile of Alzheimer's Disease: A Morphometric Coalteration Network Analysis

Gray matter alterations are typical features of brain disorders. However, they do not impact on the brain randomly. Indeed, it has been suggested that neuropathological processes can selectively affect certain assemblies of neurons, which typically are at the center of crucial functional networks. Because of their topological centrality, these areas form a core set that is more likely to be affected by neuropathological processes. In order to identify and study the pattern formed by brain alterations in patients’ with Alzheimer’s disease (AD), we devised an innovative meta-analytic method for analyzing voxel-based morphometry data. This methodology enabled us to discover that in AD gray matter alterations do not occur randomly across the brain but, on the contrary, follow identifiable patterns of distribution. This alteration pattern exhibits a network-like structure composed of coaltered areas that can be defined as coatrophy network. Within the coatrophy network of AD, we were able to further identify a core subnetwork of coaltered areas that includes the left hippocampus, left and right amygdalae, right parahippocampal gyrus, and right temporal inferior gyrus. In virtue of their network centrality, these brain areas can be thought of as pathoconnectivity hubs.

Definition and characterization of an extended multiple-demand network

Neuroimaging evidence suggests that executive functions (EF) depend on brain regions that are not closely tied to specific cognitive demands but rather to a wide range of behaviors. A multiple-demand (MD) system has been proposed, consisting of regions showing conjoint activation across multiple demands. Additionally, a number of studies defining networks specific to certain cognitive tasks suggest that the MD system may be composed of a number of sub-networks each subserving specific roles within the system. We here provide a robust definition of an extended MDN (eMDN) based on task-dependent and task-independent functional connectivity analyses seeded from regions previously shown to be convergently recruited across neuroimaging studies probing working memory, attention and inhibition, i.e., the proposed key components of EF. Additionally, we investigated potential sub-networks within the eMDN based on their connectional and functional similarities. We propose an eMDN network consisting of a core whose integrity should be crucial to performance of most operations that are considered higher cognitive or EF. This then recruits additional areas depending on specific demands.

The hippocampal network model: A transdiagnostic metaconnectomic approach

Purpose

The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM) using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH) (Seeley et al., 2009) by identifying structural and functional covariance in this hippocampal network.

Methods

To generate our network model, we used BrainMap's VBM database to perform a region-to-whole-brain (RtWB) meta-analysis of 269 VBM experiments from 165 published studies across a range of 38 psychiatric and neurological diseases reporting hippocampal gray matter density alterations. This step identified 11 significant gray matter foci, or nodes. We subsequently used meta-analytic connectivity modeling (MACM) to define edges of structural covariance between nodes from VBM data as well as functional covariance using the functional task-activation database, also from BrainMap. Finally, we applied a correlation analysis using Pearson's r to assess the similarities and differences between the structural and functional covariance models.

Key findings

Our hippocampal RtWB meta-analysis reported consistent and significant structural covariance in 11 key regions. The subsequent structural and functional MACMs showed a strong correlation between HNM nodes with a significant structural-functional covariance correlation of r = .377 (p = .000049).

Significance

This novel method of studying network covariance using VBM and functional meta-analytic techniques allows for the identification of generalizable patterns of functional and structural abnormalities pertaining to the hippocampus. In accordance with the NDH, this framework could have major implications in studying and predicting spatial disease patterns using network-based assays.

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We derived regions that structurally co-vary with the hippocampus in a network model using a transdiagnostic meta-analytic approach.

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We used meta-analytic connectivity mapping to assess inter-regional connectivity from BrainMap's structural and functional databases.

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We tested the network degeneration hypothesis by identifying network correlations between structural and functional networks.

How do morphological alterations caused by chronic pain distribute across the brain? A meta-analytic co-alteration study

•In chronic pain, gray matter (GM) alterations are not distributed randomly across the brain.•The pattern of co-alterations resembles that of brain connectivity.•The alterations' distribution partly rely on the pathways of functional connectivity.•This method allows us to identify tendencies in the distribution of GM co-alteration related to chronic pain.

White matter microstructural abnormalities in amnestic mild cognitive impairment: A meta-analysis of whole-brain and ROI-based studies

Studies that examined white matter (WM) alterations in amnestic mild cognitive impairment (aMCI) abound. This timely meta-analysis aims to synthesize the results of these studies. Seventy-seven studies (totalN_aMCI=1844) were included. Fourteen region-of-interest-based (ROI-based) (k≥8;N_aMCI≥284 per ROI) and two activation likelihood estimation (ALE) meta-analyses (fractional anisotropy [FA]: k=15;N_aMCI=463; mean diffusivity [MD]: k=8;N_aMCI=193) were carried out. Among the many significant ROI-related findings, reliable FA and MD alterations in the fornix, uncinate fasciculus, and parahippocampal cingulum were observed in aMCI. Larger effects were observed in MD relative to FA. The ALE meta-analysis revealed a significant FA decrease among aMCI subjects in the posterior corona radiata. These results provide robust evidence of the presence of WM abnormalities in aMCI. Our findings also highlight the importance of carrying out both ROI-based and whole-brain-based research to obtain a complete picture of WM microstructural alterations associated with the condition..

Gray matter asymmetries in aging and neurodegeneration: A review and meta-analysis

Inter-hemispheric asymmetries are a common phenomenon of the human brain. Some evidence suggests that neurodegeneration related to aging and disease may preferentially affect the left-usually language- and motor-dominant-hemisphere. Here, we used activation likelihood estimation meta-analysis to assess gray matter (GM) loss and its lateralization in healthy aging and in neurodegeneration, namely, mild cognitive impairment (MCI), Alzheimer's dementia (AD), Parkinson's disease (PD), and Huntington's disease (HD). This meta-analysis, comprising 159 voxel-based morphometry publications (enrolling 4,469 patients and 4,307 controls), revealed that GM decline appeared to be asymmetric at trend levels but provided no evidence for increased left-hemisphere vulnerability. Regions with asymmetric GM decline were located in areas primarily affected by neurodegeneration. In HD, the left putamen showed converging evidence for more pronounced atrophy, while no consistent pattern was found in PD. In MCI, the right hippocampus was more atrophic than its left counterpart, a pattern that reversed in AD. The stability of these findings was confirmed using permutation tests. However, due to the lenient threshold used in the asymmetry analysis, further work is needed to confirm our results and to provide a better understanding of the functional role of GM asymmetries, for instance in the context of cognitive reserve and compensation. Hum Brain Mapp 38:5890-5904, 2017. © 2017 Wiley Periodicals, Inc.

Differential Functional Connectivity Alterations of Two Subdivisions within the Right dlPFC in Parkinson's Disease

Patients suffering from Parkinson's disease (PD) often show impairments in executive function (EF) like decision-making and action control. The right dorsolateral prefrontal cortex (dlPFC) has been strongly implicated in EF in healthy subjects and has repeatedly been reported to show alterations related to EF impairment in PD. Recently, two key regions for cognitive action control have been identified within the right dlPFC by co-activation based parcellation. While the posterior region is engaged in rather basal EF like stimulus integration and working memory, the anterior region has a more abstract, supervisory function. To investigate whether these functionally distinct subdivisions of right dlPFC are differentially affected in PD, we analyzed resting-state functional connectivity (FC) in 39 PD patients and 44 age- and gender-matched healthy controls. Patients were examined both after at least 12 h withdrawal of dopaminergic drugs (OFF) and under their regular dopaminergic medication (ON). We found that only the posterior right dlPFC subdivision shows FC alterations in PD, while the anterior part remains unaffected. PD-related decreased FC with posterior right dlPFC was found in the bilateral medial posterior parietal cortex (mPPC) and left dorsal premotor region (PMd) in the OFF state. In the medical ON, FC with left PMd normalized, while decoupling with bilateral mPPC remained. Furthermore, we observed increased FC between posterior right dlPFC and the bilateral dorsomedial prefrontal cortex (dmPFC) in PD in the ON state. Our findings point to differential disturbances of right dlPFC connectivity in PD, which relate to its hierarchical organization of EF processing by stronger affecting the functionally basal posterior aspect than the hierarchically higher anterior part.

Multimodal connectivity-based parcellation reveals a shell-core dichotomy of the human nucleus accumbens

The subdifferentiation of the nucleus accumbens (NAc) has been extensively studied using neuroanatomy and histochemistry, yielding a well-accepted dichotomic shell/core architecture that reflects dissociable roles, such as in reward and aversion, respectively. However, in vivo parcellation of these structures in humans has been rare, potentially impairing future research into the structural and functional characteristics and alterations of putative NAc subregions. Here, we used three complementary parcellation schemes based on tractography, task-independent functional connectivity, and task-dependent co-activation to investigate the regional differentiation within the NAc. We found that a 2-cluster solution with shell-like and core-like subdivisions provided the best description of the data and was consistent with the earlier anatomical shell/core architecture. The consensus clusters from this optimal solution, which was based on the three schemes, were used as the final parcels for the subsequent connection analyses. The resulting connectivity patterns presented inter-hemispheric symmetry, convergence and divergence across the modalities, and, most importantly, clearly distinct patterns between the two subregions. This convergent connectivity patterns also confirmed the connections in animal models, supporting views that the two subregions could have antagonistic roles in some circumstances. Finally, the identified parcels should be helpful in further neuroimaging studies of the NAc. Hum Brain Mapp 38:3878-3898, 2017. © 2017 Wiley Periodicals, Inc.

The Right Dorsal Premotor Mosaic: Organization, Functions, and Connectivity

The right dorsal premotor cortex (PMd) of humans has been reported to be involved in a broad range of motor and cognitive functions. We explored the basis of this behavioral heterogeneity by performing a connectivity-based parcellation using meta-analytic approach applied to PMd coactivations. We compared our connectivity-based parcellation results with parcellations obtained through resting-state functional connectivity and probabilistic diffusion tractography. Functional connectivity profiles and behavioral decoding of the resulting PMd subregions allowed characterizing their respective behavior profile. These procedures divided the right PMd into 5 distinct subregions that formed a cognitive-motor gradient along a rostro-caudal axis. In particular, we found 1) a rostral subregion functionally connected with prefrontal cortex, which likely supports high-level cognitive processes, such as working memory, 2) a central subregion showing a mixed behavioral profile and functional connectivity to parietal regions of the dorsal attention network, and 3) a caudal subregion closely integrated with the motor system. Additionally, we found 4) a dorsal subregion, preferentially related to hand movements and connected to both cognitive and motor regions, and 5) a ventral subregion, whose functional profile fits the concept of an eye movement-related field. In conclusion, right PMd may be considered as a functional mosaic formed by 5 subregions.

Resting-state functional reorganization in Parkinson's disease: An activation likelihood estimation meta-analysis

Parkinson's disease (PD) is a common progressive neurodegenerative disorder. Studies using resting-state functional magnetic resonance imaging (fMRI) to investigate underlying pathophysiology of motor and non-motor symptoms in PD yielded largely inconsistent results. This quantitative neuroimaging meta-analysis aims to identify consistent abnormal intrinsic functional patterns in PD across studies. We used PubMed to retrieve suitable resting-state studies and stereotactic data were extracted from 28 individual between-group comparisons. Convergence across their findings was tested using the activation likelihood estimation (ALE) approach. We found convergent evidence for intrinsic functional disturbances in bilateral inferior parietal lobule (IPL) and the supramarginal gyrus in PD patients compared to healthy subjects. In follow-up task-based and task-independent functional connectivity (FC) analyses using two independent healthy subject data sets, we found that the regions showing convergent aberrations in PD formed an interconnected network mainly with the default mode network (DMN). Behavioral characterization of these regions using the BrainMap database suggested associated dysfunction of perception and executive processes. Taken together, our findings highlight the role of parietal cortex in the pathophysiology of PD.

Heterogeneous fractionation profiles of meta-analytic coactivation networks

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

Cortical and subcortical mechanisms of brain-machine interfaces

Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level. Here, we provide a comprehensive and detailed report of the areas active during on-line BMI control. We recorded functional magnetic resonance imaging (fMRI) data while participants controlled an EEG-based BMI inside the scanner. We identified the regions activated during BMI control and how they overlap with those involved in motor imagery (without any BMI control). In addition, we investigated which regions reflect the subjective sense of controlling a BMI, the sense of agency for BMI-actions. Our data revealed an extended cortical-subcortical network involved in operating a motor-imagery BMI. This includes not only sensorimotor regions but also the posterior parietal cortex, the insula and the lateral occipital cortex. Interestingly, the basal ganglia and the anterior cingulate cortex were involved in the subjective sense of controlling the BMI. These results inform basic neuroscience by showing that the mechanisms of BMI control extend beyond sensorimotor cortices. This knowledge may be useful for the development of BMIs that offer a more natural and embodied feeling of control for the user. Hum Brain Mapp 38:2971-2989, 2017. © 2017 Wiley Periodicals, Inc.

The heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterization

Despite the common conception of the dorsal premotor cortex (PMd) as a single brain region, its diverse connectivity profiles and behavioral heterogeneity argue for a differentiated organization of the PMd. A previous study revealed that the right PMd is characterized by a rostro-caudal and a ventro-dorsal distinction dividing it into five subregions: rostral, central, caudal, ventral and dorsal. The present study assessed whether a similar organization is present in the left hemisphere, by capitalizing on a multimodal data-driven approach combining connectivity-based parcellation (CBP) based on meta-analytic modeling, resting-state functional connectivity, and probabilistic diffusion tractography. The resulting PMd modules were then characterized based on multimodal functional connectivity and a quantitative analysis of associated behavioral functions. Analyzing the clusters consistent across all modalities revealed an organization of the left PMd that mirrored its right counterpart to a large degree. Again, caudal, central and rostral modules reflected a cognitive-motor gradient and a premotor eye-field was found in the ventral part of the left PMd. In addition, a distinct module linked to abstract cognitive functions was observed in the rostro-ventral left PMd across all CBP modalities, implying greater differentiation of higher cognitive functions for the left than the right PMd.

Correspondent Functional Topography of the Human Left Inferior Parietal Lobule at Rest and Under Task Revealed Using Resting-State fMRI and Coactivation Based Parcellation

The human left inferior parietal lobule (LIPL) plays a pivotal role in many cognitive functions and is an important node in the default mode network (DMN). Although many previous studies have proposed different parcellation schemes for the LIPL, the detailed functional organization of the LIPL and the exact correspondence between the DMN and LIPL subregions remain unclear. Mounting evidence indicates that spontaneous fluctuations in the brain are strongly associated with cognitive performance at the behavioral level. However, whether a consistent functional topographic organization of the LIPL during rest and under task can be revealed remains unknown. Here, they used resting-state functional connectivity (RSFC) and task-related coactivation patterns separately to parcellate the LIPL and identified seven subregions. Four subregions were located in the supramarginal gyrus (SMG) and three subregions were located in the angular gyrus (AG). The subregion-specific networks and functional characterization revealed that the four anterior subregions were found to be primarily involved in sensorimotor processing, movement imagination and inhibitory control, audition perception and speech processing, and social cognition, whereas the three posterior subregions were mainly involved in episodic memory, semantic processing, and spatial cognition. The results revealed a detailed functional organization of the LIPL and suggested that the LIPL is a functionally heterogeneous area. In addition, the present study demonstrated that the functional architecture of the LIPL during rest corresponds with that found in task processing. Hum Brain Mapp 38:1659-1675, 2017. © 2017 Wiley Periodicals, Inc.

Functional Decoding and Meta-analytic Connectivity Modeling in Adult Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Task-based functional magnetic resonance imaging (fMRI) studies of adult attention-deficit/hyperactivity disorder (ADHD) have revealed various ADHD-related dysfunctional brain regions, with heterogeneous findings across studies. Here, we used novel meta-analytic data-driven approaches to characterize the function and connectivity profile of ADHD-related dysfunctional regions consistently detected across studies.

METHODS

We first conducted an activation likelihood estimation meta-analysis of 24 task-based fMRI studies in adults with ADHD. Each ADHD-related dysfunctional region resulting from the activation likelihood estimation meta-analysis was then analyzed using functional decoding based on ~7500 fMRI experiments in the BrainMap database. This approach allows mapping brain regions to functions not necessarily tested in individual studies, thus suggesting possible novel functions for those regions. Additionally, ADHD-related dysfunctional regions were clustered based on their functional coactivation profiles across all the experiments stored in BrainMap (meta-analytic connectivity modeling).

RESULTS

ADHD-related hypoactivation was found in the left putamen, left inferior frontal gyrus (pars opercularis), left temporal pole, and right caudate. Functional decoding mapped the left putamen to cognitive aspects of music perception/reproduction and the left temporal lobe to language semantics; both these regions clustered together on the basis of their meta-analytic functional connectivity. Left inferior gyrus mapped to executive function tasks; right caudate mapped to both executive function tasks and music-related processes.

CONCLUSIONS

Our study provides meta-analytic support to the hypothesis that, in addition to well-known deficits in typical executive functions, impairment in processes related to music perception/reproduction and language semantics may be involved in the pathophysiology of adult ADHD.

Left inferior parietal lobe engagement in social cognition and language

Social cognition and language are two core features of the human species. Despite distributed recruitment of brain regions in each mental capacity, the left parietal lobe (LPL) represents a zone of topographical convergence. The present study quantitatively summarizes hundreds of neuroimaging studies on social cognition and language. Using connectivity-based parcellation on a meta-analytically defined volume of interest (VOI), regional coactivation patterns within this VOI allowed identifying distinct subregions. Across parcellation solutions, two clusters emerged consistently in rostro-ventral and caudo-ventral aspects of the parietal VOI. Both clusters were functionally significantly associated with social-cognitive and language processing. In particular, the rostro-ventral cluster was associated with lower-level processing facets, while the caudo-ventral cluster was associated with higher-level processing facets in both mental capacities. Contrarily, in the (less stable) dorsal parietal VOI, all clusters reflected computation of general-purpose processes, such as working memory and matching tasks, that are frequently co-recruited by social or language processes. Our results hence favour a rostro-caudal distinction of lower- versus higher-level processes underlying social cognition and language in the left inferior parietal lobe.

Left posterior-dorsal area 44 couples with parietal areas to promote speech fluency, while right area 44 activity promotes the stopping of motor responses

Area 44 is a cytoarchitectonically distinct portion of Broca's region. Parallel and overlapping large-scale networks couple with this region thereby orchestrating heterogeneous language, cognitive, and motor functions. In the context of stuttering, area 44 frequently comes into focus because structural and physiological irregularities affect developmental trajectories, stuttering severity, persistency, and etiology. A remarkable phenomenon accompanying stuttering is the preserved ability to sing. Speaking and singing are connatural behaviours recruiting largely overlapping brain networks including left and right area 44. Analysing which potential subregions of area 44 are malfunctioning in adults who stutter, and what effectively suppresses stuttering during singing, may provide a better understanding of the coordination and reorganization of large-scale brain networks dedicated to speaking and singing in general. We used fMRI to investigate functionally distinct subregions of area 44 during imagery of speaking and imaginary of humming a melody in 15 dextral males who stutter and 17 matched control participants. Our results are fourfold. First, stuttering was specifically linked to a reduced activation of left posterior-dorsal area 44, a subregion that is involved in speech production, including phonological word processing, pitch processing, working memory processes, sequencing, motor planning, pseudoword learning, and action inhibition. Second, functional coupling between left posterior area 44 and left inferior parietal lobule was deficient in stuttering. Third, despite the preserved ability to sing, males who stutter showed bilaterally a reduced activation of area 44 when imagine humming a melody, suggesting that this fluency-enhancing condition seems to bypass posterior-dorsal area 44 to achieve fluency. Fourth, time courses of the posterior subregions in area 44 showed delayed peak activations in the right hemisphere in both groups, possibly signaling the offset response. Because these offset response-related activations in the right hemisphere were comparably large in males who stutter, our data suggest a hyperactive mechanism to stop speech motor responses and thus possibly reflect a pathomechanism, which, until now, has been neglected. Overall, the current results confirmed a recently described co-activation based parcellation supporting the idea of functionally distinct subregions of left area 44.

Implementation errors in the GingerALE Software: Description and recommendations

Neuroscience imaging is a burgeoning, highly sophisticated field the growth of which has been fostered by grant-funded, freely distributed software libraries that perform voxel-wise analyses in anatomically standardized three-dimensional space on multi-subject, whole-brain, primary datasets. Despite the ongoing advances made using these non-commercial computational tools, the replicability of individual studies is an acknowledged limitation. Coordinate-based meta-analysis offers a practical solution to this limitation and, consequently, plays an important role in filtering and consolidating the enormous corpus of functional and structural neuroimaging results reported in the peer-reviewed literature. In both primary data and meta-analytic neuroimaging analyses, correction for multiple comparisons is a complex but critical step for ensuring statistical rigor. Reports of errors in multiple-comparison corrections in primary-data analyses have recently appeared. Here, we report two such errors in GingerALE, a widely used, US National Institutes of Health (NIH)-funded, freely distributed software package for coordinate-based meta-analysis. These errors have given rise to published reports with more liberal statistical inferences than were specified by the authors. The intent of this technical report is threefold. First, we inform authors who used GingerALE of these errors so that they can take appropriate actions including re-analyses and corrective publications. Second, we seek to exemplify and promote an open approach to error management. Third, we discuss the implications of these and similar errors in a scientific environment dependent on third-party software. Hum Brain Mapp 38:7-11, 2017. © 2016 Wiley Periodicals, Inc.

Connectome-scale assessment of structural and functional connectivity in mild traumatic brain injury at the acute stage

Mild traumatic brain injury (mTBI) accounts for over one million emergency visits each year in the United States. The large-scale structural and functional network connectivity changes of mTBI are still unknown. This study was designed to determine the connectome-scale brain network connectivity changes in mTBI at both structural and functional levels. 40 mTBI patients at the acute stage and 50 healthy controls were recruited. A novel approach called Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs) was applied for connectome-scale analysis of both diffusion tensor imaging and resting state functional MRI data. Among 358 networks identified on DICCCOL analysis, 41 networks were identified as structurally discrepant between patient and control groups. The involved major white matter tracts include the corpus callosum, and superior and inferior longitudinal fasciculi. Functional connectivity analysis identified 60 connectomic signatures that differentiate patients from controls with 93.75% sensitivity and 100% specificity. Analysis of functional domains showed decreased intra-network connectivity within the emotion network and among emotion-cognition interactions, and increased interactions among action-emotion and action-cognition as well as within perception networks. This work suggests that mTBI may result in changes of structural and functional connectivity on a connectome scale at the acute stage.

Chronic cigarette smoking is linked with structural alterations in brain regions showing acute nicotinic drug-induced functional modulations

Background

Whereas acute nicotine administration alters brain function which may, in turn, contribute to enhanced attention and performance, chronic cigarette smoking is linked with regional brain atrophy and poorer cognition. However, results from structural magnetic resonance imaging (MRI) studies comparing smokers versus nonsmokers have been inconsistent and measures of gray matter possess limited ability to inform functional relations or behavioral implications. The purpose of this study was to address these interpretational challenges through meta-analytic techniques in the service of clarifying the impact of chronic smoking on gray matter integrity and more fully contextualizing such structural alterations.

Methods

We first conducted a coordinate-based meta-analysis of structural MRI studies to identify consistent structural alterations associated with chronic smoking. Subsequently, we conducted two additional meta-analytic assessments to enhance insight into potential functional and behavioral relations. Specifically, we performed a multimodal meta-analytic assessment to test the structural–functional hypothesis that smoking-related structural alterations overlapped those same regions showing acute nicotinic drug-induced functional modulations. Finally, we employed database driven tools to identify pairs of structurally impacted regions that were also functionally related via meta-analytic connectivity modeling, and then delineated behavioral phenomena associated with such functional interactions via behavioral decoding.

Results

Across studies, smoking was associated with convergent structural decreases in the left insula, right cerebellum, parahippocampus, multiple prefrontal cortex (PFC) regions, and the thalamus. Indicating a structural–functional relation, we observed that smoking-related gray matter decreases overlapped with the acute functional effects of nicotinic agonist administration in the left insula, ventromedial PFC, and mediodorsal thalamus. Suggesting structural-behavioral implications, we observed that the left insula’s task-based, functional interactions with multiple other structurally impacted regions were linked with pain perception, the right cerebellum’s interactions with other regions were associated with overt body movements, interactions between the parahippocampus and thalamus were linked with memory processes, and interactions between medial PFC regions were associated with face processing.

Conclusions

Collectively, these findings emphasize brain regions (e.g., ventromedial PFC, insula, thalamus) critically linked with cigarette smoking, suggest neuroimaging paradigms warranting additional consideration among smokers (e.g., pain processing), and highlight regions in need of further elucidation in addiction (e.g., cerebellum).

Electronic supplementary material

The online version of this article (doi:10.1186/s12993-016-0100-5) contains supplementary material, which is available to authorized users.

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture

The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.

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

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

Structural and functional neural adaptations in obstructive sleep apnea: An activation likelihood estimation meta-analysis

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The right basolateral amygdala, the hippocampus and the right insular cortex are important nodes in obstructive sleep apnea (OSA).

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Functional characterization of these regions suggested associated dysfunction of emotional, sensory, and limbic processes in OSA.

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Connectivity analysis demonstrated that these regions are part of a joint network comprising the anterior insula, posterior-medial frontal cortex and thalamus.

Obstructive sleep apnea (OSA) is a common multisystem chronic disorder. Functional and structural neuroimaging has been widely applied in patients with OSA, but these studies have often yielded diverse results. The present quantitative meta-analysis aims to identify consistent patterns of abnormal activation and grey matter loss in OSA across studies. We used PubMed to retrieve task/resting-state functional magnetic resonance imaging and voxel-based morphometry studies. Stereotactic data were extracted from fifteen studies, and subsequently tested for convergence using activation likelihood estimation. We found convergent evidence for structural atrophy and functional disturbances in the right basolateral amygdala/hippocampus and the right central insula. Functional characterization of these regions using the BrainMap database suggested associated dysfunction of emotional, sensory, and limbic processes. Assessment of task-based co-activation patterns furthermore indicated that the two regions obtained from the meta-analysis are part of a joint network comprising the anterior insula, posterior-medial frontal cortex and thalamus. Taken together, our findings highlight the role of right amygdala, hippocampus and insula in the abnormal emotional and sensory processing in OSA.

Connectivity-based parcellation increases network detection sensitivity in resting state fMRI: An investigation into the cingulate cortex in autism

Although resting state fMRI (RS-fMRI) is increasingly used to generate biomarkers of psychiatric illnesses, analytical choices such as seed size and placement can lead to variable findings. Seed placement especially impacts on RS-fMRI studies of Autism Spectrum Disorder (ASD), because individuals with ASD are known to possess more variable network topographies. Here, we present a novel pipeline for analysing RS-fMRI in ASD using the cingulate cortex as an exemplar anatomical region of interest. Rather than using seeds based on previous literature, or gross morphology, we used a combination of structural information, task-independent (RS-fMRI) and task-dependent functional connectivity (Meta-Analytic Connectivity Modeling) to partition the cingulate cortex into six subregions with unique connectivity fingerprints and diverse behavioural profiles. This parcellation was consistent between groups and highly replicable across individuals (up to 93% detection) suggesting that the organisation of cortico-cingulo connections is highly similar between groups. However, our results showed an age-related increase in connectivity between the anterior middle cingulate cortex and right lateral prefrontal cortex in ASD, whilst this connectivity decreased in controls. There was also a Group × Grey Matter (GM) interaction, showing increased connectivity between the anterior cingulate cortex and the rectal gyrus in concert with increasing rectal gyrus GM in controls. By comparing our approach to previously established methods we revealed that our approach improves network detection in both groups, and that the ability to detect group differences using 4 mm radius spheres varies greatly with seed placement. Using our multi-modal approach we find disrupted cortico-cingulo circuits that, based on task-dependent information, may contribute to ASD deficits in attention and social interaction. Moreover, we highlight how more sensitive approaches to RS-fMRI are crucial for establishing robust and reproducible connectivity-based biomarkers in psychiatric disorders.

General and specialized brain correlates for analogical reasoning: A meta-analysis of functional imaging studies

Reasoning by analogy allows us to link distinct domains of knowledge and to transfer solutions from one domain to another. Analogical reasoning has been studied using various tasks that have generally required the consideration of the relationships between objects and their integration to infer an analogy schema. However, these tasks varied in terms of the level and the nature of the relationships to consider (e.g., semantic, visuospatial). The aim of this study was to identify the cerebral network involved in analogical reasoning and its specialization based on the domains of information and task specificity. We conducted a coordinate-based meta-analysis of 27 experiments that used analogical reasoning tasks. The left rostrolateral prefrontal cortex was one of the regions most consistently activated across the studies. A comparison between semantic and visuospatial analogy tasks showed both domain-oriented regions in the inferior and middle frontal gyri and a domain-general region, the left rostrolateral prefrontal cortex, which was specialized for analogy tasks. A comparison of visuospatial analogy to matrix problem tasks revealed that these two relational reasoning tasks engage, at least in part, distinct right and left cerebral networks, particularly separate areas within the left rostrolateral prefrontal cortex. These findings highlight several cognitive and cerebral differences between relational reasoning tasks that can allow us to make predictions about the respective roles of distinct brain regions or networks. These results also provide new, testable anatomical hypotheses about reasoning disorders that are induced by brain damage. Hum Brain Mapp 37:1953-1969, 2016. © 2016 Wiley Periodicals, Inc.

Meta-analytic connectivity modeling of the human superior temporal sulcus

The superior temporal sulcus (STS) is a critical region for multiple neural processes in the human brain Hein and Knight (J Cogn Neurosci 20(12): 2125-2136, 2008). To better understand the multiple functions of the STS it would be useful to know more about its consistent functional coactivations with other brain regions. We used the meta-analytic connectivity modeling technique to determine consistent functional coactivation patterns across experiments and behaviors associated with bilateral anterior, middle, and posterior anatomical STS subregions. Based on prevailing models for the cortical organization of audition and language, we broadly hypothesized that across various behaviors the posterior STS (pSTS) would coactivate with dorsal-stream regions, whereas the anterior STS (aSTS) would coactivate with ventral-stream regions. The results revealed distinct coactivation patterns for each STS subregion, with some overlap in the frontal and temporal areas, and generally similar coactivation patterns for the left and right STS. Quantitative comparison of STS subregion coactivation maps demonstrated that the pSTS coactivated more strongly than other STS subregions in the same hemisphere with dorsal-stream regions, such as the inferior parietal lobule (only left pSTS), homotopic pSTS, precentral gyrus and supplementary motor area. In contrast, the aSTS showed more coactivation with some ventral-stream regions, such as the homotopic anterior temporal cortex and left inferior frontal gyrus, pars orbitalis (only right aSTS). These findings demonstrate consistent coactivation maps across experiments and behaviors for different anatomical STS subregions, which may help future studies consider various STS functions in the broader context of generalized coactivations for individuals with and without neurological disorders.

Effects of Stimulus Type and Strategy on Mental Rotation Network: An Activation Likelihood Estimation Meta-Analysis

We can predict how an object would look like if we were to see it from different viewpoints. The brain network governing mental rotation (MR) has been studied using a variety of stimuli and tasks instructions. By using activation likelihood estimation (ALE) meta-analysis we tested whether different MR networks can be modulated by the type of stimulus (body vs. non-body parts) or by the type of tasks instructions (motor imagery-based vs. non-motor imagery-based MR instructions). Testing for the bodily and non-bodily stimulus axis revealed a bilateral sensorimotor activation for bodily-related as compared to non-bodily-related stimuli and a posterior right lateralized activation for non-bodily-related as compared to bodily-related stimuli. A top-down modulation of the network was exerted by the MR tasks instructions with a bilateral (preferentially sensorimotor left) network for motor imagery- vs. non-motor imagery-based MR instructions and the latter activating a preferentially posterior right occipito-temporal-parietal network. The present quantitative meta-analysis summarizes and amends previous descriptions of the brain network related to MR and shows how it is modulated by top-down and bottom-up experimental factors.