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

Coordinate based random effect size meta-analysis of neuroimaging studies

Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density. Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely.

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.

Does pediatric post-traumatic stress disorder alter the brain? Systematic review and meta-analysis of structural and functional magnetic resonance imaging studies

Several studies have recently demonstrated that the volumes of specific brain regions are reduced in children and adolescents with post-traumatic stress disorder (PTSD) compared with those of healthy controls. Our study investigated the potential association between early traumatic experiences and altered brain regions and functions. We conducted a systematic review of the scientific literature regarding functional magnetic resonance imaging and a meta-analysis of structural magnetic resonance imaging studies that investigated cerebral region volumes in pediatric patients with PTSD. We searched for articles from 2000 to 2014 in the PsycINFO, PubMed, Medline, Lilacs, and ISI (Web of Knowledge) databases. All data regarding the amygdala, hippocampus, corpus callosum, brain, and intracranial volumes that fit the inclusion criteria were extracted and combined in a meta-analysis that assessed differences between groups. The meta-analysis found reduced total corpus callosum areas and reduced total cerebral and intracranial volumes in the patients with PTSD. The total hippocampus (left and right hippocampus) and gray matter volumes of the amygdala and frontal lobe were also reduced, but these differences were not significant. The functional studies revealed differences in brain region activation in response to stimuli in the post-traumatic stress symptoms/PTSD group. Our results confirmed that the pediatric patients with PTSD exhibited structural and functional brain abnormalities and that some of the abnormalities occurred in different brain regions than those observed in adults.

Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: A multimodal meta-analysis

BACKGROUND

In healthy subjects, there is an association between amplitude of low-frequency fluctuations (ALFF) and regional cerebral blood flow (rCBF). To date, no published meta-analysis has investigated changes in the regional ALFF in medication-free depressed patients.

METHODS

In this study, we aimed to explore whether resting-state rCBF and ALFF changes co-occur in the depressed brain without the potential confound of medication. Using signed differential mapping (SDM), we conducted two meta-analyses, one of rCBF studies and one of ALFF studies, involving medication-free patients with major depressive disorder (MDD). In addition, we conducted a multimodal meta-analysis to identify brain regions that showed abnormalities in both rCBF and ALFF.

RESULTS

A total of 16 studies were included in this series. We identified abnormalities in resting-state rCBF and ALFF in the left insula in medication-free MDD patients compared with healthy controls (HC). In addition, we observed altered resting-state rCBF in the limbic-subcortical-cortical circuit and altered ALFF in the default mode network (DMN) and some motor-related brain regions.

LIMITATIONS

The analysis techniques, patient characteristics and clinical variables of the included studies were heterogeneous.

CONCLUSIONS

The conjoint alterations in ALFF and rCBF in the left insula may represent core neuropathological changes in medication-free patients with MDD and merit further studying.

Best practices in data analysis and sharing in neuroimaging using MRI

Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks

Previous research has provided qualitative evidence for overlap in a number of brain regions across the subjective value network (SVN) and the default mode network (DMN). In order to quantitatively assess this overlap, we conducted a series of coordinate-based meta-analyses (CBMA) of results from 466 functional magnetic resonance imaging experiments on task-negative or subjective value-related activations in the human brain. In these analyses, we first identified significant overlaps and dissociations across activation foci related to SVN and DMN. Second, we investigated whether these overlapping subregions also showed similar patterns of functional connectivity, suggesting a shared functional subnetwork. We find considerable overlap between SVN and DMN in subregions of central ventromedial prefrontal cortex (cVMPFC) and dorsal posterior cingulate cortex (dPCC). Further, our findings show that similar patterns of bidirectional functional connectivity between cVMPFC and dPCC are present in both networks. We discuss ways in which our understanding of how subjective value (SV) is computed and represented in the brain can be synthesized with what we know about the DMN, mind-wandering, and self-referential processing in light of our findings.

Sharing brain mapping statistical results with the neuroimaging data model

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html.

Identifying Activation Centers with Spatial Cox Point Processes Using fMRI Data

We developed a Bayesian clustering method to identify significant regions of brain activation. Coordinate-based meta data originating from functional magnetic resonance imaging (fMRI) were of primary interest. Individual fMRI has the ability to measure the intensity of blood flow and oxygen to a location within the brain that was activated by a given thought or emotion. The proposed method performed clustering on two levels, latent foci center and study activation center, with a spatial Cox point process utilizing the Dirichlet process to describe the distribution of foci. Intensity was modeled as a function of distance between the focus and the center of the cluster of foci using a Gaussian kernel. Simulation studies were conducted to evaluate the sensitivity and robustness of the method with respect to cluster identification and underlying data distributions. We applied the method to a meta data set to identify emotion foci centers.

Neural signatures of social conformity: A coordinate-based activation likelihood estimation meta-analysis of functional brain imaging studies

People often align their behaviors with group opinions, known as social conformity. Many neuroscience studies have explored the neuropsychological mechanisms underlying social conformity. Here we employed a coordinate-based meta-analysis on neuroimaging studies of social conformity with the purpose to reveal the convergence of the underlying neural architecture. We identified a convergence of reported activation foci in regions associated with normative decision-making, including ventral striatum (VS), dorsal posterior medial frontal cortex (dorsal pMFC), and anterior insula (AI). Specifically, consistent deactivation of VS and activation of dorsal pMFC and AI are identified when people's responses deviate from group opinions. In addition, the deviation-related responses in dorsal pMFC predict people's conforming behavioral adjustments. These are consistent with current models that disagreement with others might evoke "error" signals, cognitive imbalance, and/or aversive feelings, which are plausibly detected in these brain regions as control signals to facilitate subsequent conforming behaviors. Finally, group opinions result in altered neural correlates of valuation, manifested as stronger responses of VS to stimuli endorsed than disliked by others.

Meta-analysis of psychophysiological interactions: Revisiting cluster-level thresholding and sample sizes

Within the neuroimaging community, coordinate based meta-analyses (CBMAs) are essential for aggregating findings across studies and testing whether those studies report similar anatomical locations. This approach has been predominantly applied to studies that focus on whether activation of a brain region is associated with a given psychological process. In a recent paper, we used CBMA to examine a distinct set of studies-that is, those focusing on whether connectivity between brain regions is modulated by a given psychological process (Smith et al. [2016]: Hum Brain Mapp 37:2904-2917). Specifically, we reviewed 284 studies examining brain connectivity with psychophysiological interactions (PPI). Our meta-analytic results indicated that PPI yields connectivity patterns that are consistent across studies and that can be specific for a given psychological process and seed region. After publication of our findings, we learned that the analysis software we used to conduct our CBMAs (GingerALE v2.3.3) contained an implementation error that led to results that were more liberal than intended. Here, we comment on the impact of this implementation error on the results of our paper, new recommendations for sample sizes in CBMAs, and the importance of communication between software users and developers. We show that our key claims are supported in a reanalysis and that our results are robust to new guidelines on sample sizes. Hum Brain Mapp 38:588-591, 2017. © 2016 Wiley Periodicals, Inc.

Brain gray matter alterations and associated demographic profiles in adults with autism spectrum disorder: A meta-analysis of voxel-based morphometry studies

BACKGROUND

There is increasing evidence that children with autism spectrum disorder are accompanied by specific anatomical alterations. However, the anatomical abnormalities in adults with autism spectrum disorder are poorly understood. This study was aimed to identify the neuroanatomical substrates underlying the pathophysiology of adults with autism spectrum disorder. We also investigated the relationship between neuroanatomical alterations and clinical and demographic characteristics.

METHODS

A total of 13 datasets were enrolled, of which 12 studies compared whole-brain differences of 382 adult patients with autism and 393 healthy control subjects. We conducted a meta-analysis to quantitatively estimate regional gray matter volume abnormalities in individuals with autism using the effect-size signed differential mapping.

RESULTS

The voxel-wise meta-analysis revealed that relative to controls, adults with autism spectrum disorder had significantly increased gray matter volume in the middle temporal gyrus, superior temporal gyrus, postcentral gyrus and parahippocampal gyrus, and reduced gray matter volume in the anterior cingulate cortex and cerebellum. Variations in gray matter volume were significantly associated with the mean age and mean total IQ score of the patients, as well as with the percentage of male patients with autism.

CONCLUSION

These findings confirmed that the neuroanatomical alterations in the fronto-temporal cortices, limbic system and cerebellum in adult individuals with autism were different from the children and young adolescent's autism. The effects of demographic characteristics on the brain morphological changes allow us to further clarify the neurobiological mechanisms and developmental trajectory in adult population with autism spectrum disorder.

The neural mechanisms of hallucinations: A quantitative meta-analysis of neuroimaging studies

Activation likelihood estimation meta-analysis of functional neuroimaging data was used to investigate the neural mechanisms underlying auditory-verbal and visual hallucinations (AVHs and VHs). Consistent activation across studies during AVHs, but not VHs, in Wernicke's and Broca's areas is consistent with involvement of speech and language processes in the experience of hearing voices when none are present. Similarly, greater activity in auditory cortex during AVHs and in visual cortex during VHs supports models proposing over-stimulation of sensory cortices in the generation of these perceptual anomalies. Activation across studies in the medial temporal lobe highlights a role for memory intrusions in the provision of content for AVHs, whereas insula activation may relate to the involvement of awareness and self-representation. Finally, activation in the paracingulate region of medial prefrontal cortex during AVHs is consistent with models implicating reality monitoring impairment in the misattribution of self-generated information as externally perceived. In the light of the results, the need for unified theoretical frameworks that account for the full range of hallucinatory experiences is discussed.

ABERRANT RESTING-STATE BRAIN ACTIVITY IN POSTTRAUMATIC STRESS DISORDER: A META-ANALYSIS AND SYSTEMATIC REVIEW

BACKGROUND

About 10% of trauma-exposed individuals develop PTSD. Although a growing number of studies have investigated resting-state abnormalities in PTSD, inconsistent results suggest a need for a meta-analysis and a systematic review.

METHODS

We conducted a systematic literature search in four online databases using keywords for PTSD, functional neuroimaging, and resting-state. In total, 23 studies matched our eligibility criteria. For the meta-analysis, we included 14 whole-brain resting-state studies, reporting data on 663 participants (298 PTSD patients and 365 controls). We used the activation likelihood estimation approach to identify concurrence of whole-brain hypo- and hyperactivations in PTSD patients during rest. Seed-based studies could not be included in the quantitative meta-analysis. Therefore, a separate qualitative systematic review was conducted on nine seed-based functional connectivity studies.

RESULTS

The meta-analysis showed consistent hyperactivity in the ventral anterior cingulate cortex and the parahippocampus/amygdala, but hypoactivity in the (posterior) insula, cerebellar pyramis and middle frontal gyrus in PTSD patients, compared to healthy controls. Partly concordant with these findings, the systematic review on seed-based functional connectivity studies showed enhanced salience network (SN) connectivity, but decreased default mode network (DMN) connectivity in PTSD.

CONCLUSIONS

Combined, these altered resting-state connectivity and activity patterns could represent neurobiological correlates of increased salience processing and hypervigilance (SN), at the cost of awareness of internal thoughts and autobiographical memory (DMN) in PTSD. However, several discrepancies between findings of the meta-analysis and systematic review were observed, stressing the need for future studies on resting-state abnormalities in PTSD patients.

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.

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.

Functional reorganisation in chronic pain and neural correlates of pain sensitisation: A coordinate based meta-analysis of 266 cutaneous pain fMRI studies

Maladaptive mechanisms of pain processing in chronic pain conditions (CP) are poorly understood. We used coordinate based meta-analysis of 266 fMRI pain studies to study functional brain reorganisation in CP and experimental models of hyperalgesia. The pattern of nociceptive brain activation was similar in CP, hyperalgesia and normalgesia in controls. However, elevated likelihood of activation was detected in the left putamen, left frontal gyrus and right insula in CP comparing stimuli of the most painful vs. other site. Meta-analysis of contrast maps showed no difference between CP, controls, mood conditions. In contrast, experimental hyperalgesia induced stronger activation in the bilateral insula, left cingulate and right frontal gyrus. Activation likelihood maps support a shared neural pain signature of cutaneous nociception in CP and controls. We also present a double dissociation between neural correlates of transient and persistent pain sensitisation with general increased activation intensity but unchanged pattern in experimental hyperalgesia and, by contrast, focally increased activation likelihood, but unchanged intensity, in CP when stimulated at the most painful body part.

Inter-site and inter-scanner diffusion MRI data harmonization

We propose a novel method to harmonize diffusion MRI data acquired from multiple sites and scanners, which is imperative for joint analysis of the data to significantly increase sample size and statistical power of neuroimaging studies. Our method incorporates the following main novelties: i) we take into account the scanner-dependent spatial variability of the diffusion signal in different parts of the brain; ii) our method is independent of compartmental modeling of diffusion (e.g., tensor, and intra/extra cellular compartments) and the acquired signal itself is corrected for scanner related differences; and iii) inter-subject variability as measured by the coefficient of variation is maintained at each site. We represent the signal in a basis of spherical harmonics and compute several rotation invariant spherical harmonic features to estimate a region and tissue specific linear mapping between the signal from different sites (and scanners). We validate our method on diffusion data acquired from seven different sites (including two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. Since the extracted rotation invariant spherical harmonic features depend on the accuracy of the brain parcellation provided by Freesurfer, we propose a feature based refinement of the original parcellation such that it better characterizes the anatomy and provides robust linear mappings to harmonize the dMRI data. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across multiple sites before and after data harmonization. We also show results using tract-based spatial statistics before and after harmonization for independent validation of the proposed methodology. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.

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.

Functional neuroanatomy of meditation: A review and meta-analysis of 78 functional neuroimaging investigations

Meditation is a family of mental practices that encompasses a wide array of techniques employing distinctive mental strategies. We systematically reviewed 78 functional neuroimaging (fMRI and PET) studies of meditation, and used activation likelihood estimation to meta-analyze 257 peak foci from 31 experiments involving 527 participants. We found reliably dissociable patterns of brain activation and deactivation for four common styles of meditation (focused attention, mantra recitation, open monitoring, and compassion/loving-kindness), and suggestive differences for three others (visualization, sense-withdrawal, and non-dual awareness practices). Overall, dissociable activation patterns are congruent with the psychological and behavioral aims of each practice. Some brain areas are recruited consistently across multiple techniques-including insula, pre/supplementary motor cortices, dorsal anterior cingulate cortex, and frontopolar cortex-but convergence is the exception rather than the rule. A preliminary effect-size meta-analysis found medium effects for both activations (d=0.59) and deactivations (d=-0.74), suggesting potential practical significance. Our meta-analysis supports the neurophysiological dissociability of meditation practices, but also raises many methodological concerns and suggests avenues for future research.

Cross-modal representations of first-hand and vicarious pain, disgust and fairness in insular and cingulate cortex

The anterior insula (AI) and mid-anterior cingulate cortex (mACC) have repeatedly been implicated in first-hand and vicarious experiences of pain, disgust and unfairness. However, it is debated whether these regions process different aversive events through a common modality-independent code, reflecting the shared unpleasantness of the experiences or through independent modality-specific representations. Using functional magnetic resonance imaging, we subjected 19 participants (and 19 confederates) to equally unpleasant painful and disgusting stimulations, as well as unfair monetary treatments. Multivoxel pattern analysis identified modality-independent activation maps in the left AI and mACC, pointing to common coding of affective unpleasantness, but also response patterns specific for the events' sensory properties and the person to whom it was addressed, particularly in the right AI. Our results provide evidence of both functional specialization and integration within AI and mACC, and support a comprehensive role of this network in processing aversive experiences for self and others.

Comorbidity Factors and Brain Mechanisms Linking Chronic Stress and Systemic Illness

Neuropsychiatric symptoms and mental illness are commonly present in patients with chronic systemic diseases. Mood disorders, such as depression, are present in up to 50% of these patients, resulting in impaired physical recovery and more intricate treatment regimen. Stress associated with both physical and emotional aspects of systemic illness is thought to elicit detrimental effects to initiate comorbid mental disorders. However, clinical reports also indicate that the relationship between systemic and psychiatric illnesses is bidirectional, further increasing the complexity of the underlying pathophysiological processes. In this review, we discuss the recent evidence linking chronic stress and systemic illness, such as activation of the immune response system and release of common proinflammatory mediators. Altogether, discovery of new targets is needed for development of better treatments for stress-related psychiatric illnesses as well as improvement of mental health aspects of different systemic diseases.

Learning to identify CNS drug action and efficacy using multistudy fMRI data

The therapeutic effects of centrally acting pharmaceuticals can manifest gradually and unreliably in patients, making the drug discovery process slow and expensive. Biological markers providing early evidence for clinical efficacy could help prioritize development of the more promising drug candidates. A potential source of such markers is functional magnetic resonance imaging (fMRI), a noninvasive imaging technique that can complement molecular imaging. fMRI has been used to characterize how drugs cause changes in brain activity. However, variation in study protocols and analysis techniques has made it difficult to identify consistent associations between subtle modulations of brain activity and clinical efficacy. We present and validate a general protocol for functional imaging-based assessment of drug activity in the central nervous system. The protocol uses machine learning methods and data from multiple published studies to identify reliable associations between drug-related activity modulations and drug efficacy, which can then be used to assess new data. A proof-of-concept version of this approach was developed and is shown here for analgesics (pain medication), and validated with eight separate studies of analgesic compounds. Our results show that the systematic integration of multistudy data permits the generalized inferences required for drug discovery. Multistudy integrative strategies of this type could help optimize the drug discovery and validation pipeline.

Neural Correlates of Disturbed Emotion Processing in Borderline Personality Disorder: A Multimodal Meta-Analysis

BACKGROUND

Disturbances in the processing and regulation of emotions are core symptoms of borderline personality disorder (BPD). To further elucidate neural underpinnings of BPD, the present meta-analysis summarizes functional neuroimaging findings of emotion processing tasks, as well as structural neuroimaging findings, and investigates multimodally affected brain regions.

METHODS

Combined coordinate- and image-based meta-analyses were calculated using anisotropic effect size signed differential mapping. Nineteen functional neuroimaging studies investigating the processing of negative compared with neutral stimuli in a total of 281 patients with BPD and 293 healthy control subjects (HC) were included. In addition, 10 studies investigating gray matter abnormalities in 263 patients with BPD and 278 HC were analyzed.

RESULTS

Compared with HC, BPD patients showed relatively increased activation of the left amygdala and posterior cingulate cortex, along with blunted responses of the bilateral dorsolateral prefrontal cortex, during the processing of negative emotional stimuli. The multimodal analysis identified the left amygdala to be characterized by a combination of functional hyperactivity and smaller gray matter volume compared with HC. Hyperresponsivity of the amygdala was moderated by medication status of the patient samples. Medication-free samples were characterized by limbic hyperactivity, whereas no such group differences were found in patients currently taking psychotropic medication.

CONCLUSIONS

Results strengthen the assumption that dysfunctional dorsolateral prefrontal and limbic brain regions are a hallmark feature of BPD and therefore are consistent with the conceptualization of BPD as an emotion dysregulation disorder.

Cross-modal and modality-specific expectancy effects between pain and disgust

Pain sensitivity increases when a noxious stimulus is preceded by cues predicting higher intensity. However, it is unclear whether the modulation of nociception by expectancy is sensory-specific ("modality based") or reflects the aversive-affective consequence of the upcoming event ("unpleasantness"), potentially common with other negative events. Here we compared expectancy effects for pain and disgust by using different, but equally unpleasant, nociceptive (thermal) and olfactory stimulations. Indeed both pain and disgust are aversive, associated with threat to the organism, and processed in partly overlapping brain networks. Participants saw cues predicting the unpleasantness (high/low) and the modality (pain/disgust) of upcoming thermal or olfactory stimulations, and rated the associated unpleasantness after stimuli delivery. Results showed that identical thermal stimuli were perceived as more unpleasant when preceded by cues threatening about high (as opposed to low) pain. A similar expectancy effect was found for olfactory disgust. Critically, cross-modal expectancy effects were observed on inconsistent trials when thermal stimuli were preceded by high-disgust cues or olfactory stimuli preceded by high-pain cues. However, these effects were stronger in consistent than inconsistent conditions. Taken together, our results suggest that expectation of an unpleasant event elicits representations of both its modality-specific properties and its aversive consequences.

Brain gray matter alterations in first episodes of depression: A meta-analysis of whole-brain studies

Though numerous studies have implicated structural abnormalities in chronically depressive patients, relatively little attention has been paid to the brain alterations in patients experiencing first episode depression (FED). The investigation of FED is important for elucidating the core pathophysiology of this disease independent of other potentially confounding factors. The present study was to provide a quantitative voxelwise meta-analysis of gray matter (GM) changes in FED using effect-size signed differential mapping (ES-SDM). The pooled meta-analysis revealed GM reductions in the right supplementary motor area, left insula, and right middle temporal gyrus in FED patients compared with the healthy controls. No GM volume increases were found. The meta-regression analyses showed that studies including patients with higher HDRS scores were significantly more likely to present reduced GM volumes in the right amygdala. This meta-analysis indicates that FED patients have significantly and robustly reduced gray matter mainly associated with emotion regulation and sensorimotor areas alterations may be specific changes in early stage of this disease.

Voxel-wise meta-analyses of brain blood flow and local synchrony abnormalities in medication-free patients with major depressive disorder

BACKGROUND

Published meta-analyses of resting-state regional cerebral blood flow (rCBF) studies of major depressive disorder (MDD) have included patients receiving antidepressants, which might affect brain activity and thus bias the results. To our knowledge, no meta-analysis has investigated regional homogeneity changes in medication-free patients with MDD. Moreover, an association between regional homogeneity and rCBF has been demonstrated in some brain regions in healthy controls. We sought to explore to what extent resting-state rCBF and regional homogeneity changes co-occur in the depressed brain without the potential confound of medication.

METHODS

Using the effect-size signed differential mapping method, we conducted 2 meta-analyses of rCBF and regional homogeneity studies of medication-free patients with MDD.

RESULTS

Our systematic search identified 14 rCBF studies and 9 regional homogeneity studies. We identified conjoint decreases in resting-state rCBF and regional homogeneity in the insula and superior temporal gyrus in medication-free patients with MDD compared with controls. Other changes included altered resting-state rCBF in the precuneus and in the frontal-limbic-thalamic-striatal neural circuit as well as altered regional homogeneity in the uncus and parahippocampal gyrus. Meta-regression revealed that the percentage of female patients with MDD was negatively associated with resting-state rCBF in the right anterior cingulate cortex and that the age of patients with MDD was negatively associated with rCBF in the left insula and with regional homogeneity in the left uncus.

LIMITATIONS

The analysis techniques, patient characteristics and clinical variables of the included studies were heterogeneous.

CONCLUSION

The conjoint alterations of rCBF and regional homogeneity in the insula and superior temporal gyrus may be core neuropathological changes in medication-free patients with MDD and serve as a specific region of interest for further studies on MDD.

A systematic review of MRI studies examining the relationship between physical fitness and activity and the white matter of the ageing brain

Higher levels of physical fitness or activity (PFA) have been shown to have beneficial effects on cognitive function and grey matter volumes in older adults. However, the relationship between PFA and the brain's white matter (WM) is not yet well established. Here, we aim to provide a comprehensive and systematic review of magnetic resonance imaging studies examining the effects of PFA on the WM of the ageing brain. Twenty-nine studies were included in the review: eleven examined WM volume, fourteen WM lesions, and nine WM microstructure. While many studies found that higher levels of PFA were associated with greater WM volumes, reduced volume or severity of WM lesions, or improved measures of WM microstructure, a number of negative findings have also been published. Meta-analyses of global measures of WM volume and WM lesion volume yielded significant, but small, effect sizes. Overall, we found evidence for cautious support of links between PFA and WM structure, and highlighted key areas for future research including the extent to which the relationship between PFA and WM structure is anatomically specific, the influence of possible confounding factors, and the relationship between PFA, WM and cognition.

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We review MRI studies of PFA and the WM of the ageing brain.

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Higher levels of PFA were often associated with improved WM outcomes.

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Meta-analyses yielded significant, but small, effect sizes.

Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners

Harmonizing diffusion MRI (dMRI) images across multiple sites is imperative for joint analysis of the data to significantly increase the sample size and statistical power of neuroimaging studies. In this work, we develop a method to harmonize diffusion MRI data across multiple sites and scanners that incorporates two main novelties: i) we take into account the spatial variability of the signal (for different sites) in different parts of the brain as opposed to existing methods, which consider one linear statistical covariate for the entire brain; ii) our method is model-free, in that no a-priori model of diffusion (e.g., tensor, compartmental models, etc.) is assumed and the signal itself is corrected for scanner related differences. We use spherical harmonic basis functions to represent the signal and compute several rotation invariant features, which are used to estimate a regionally specific linear mapping between signal from different sites (and scanners). We validate our method on diffusion data acquired from four different sites (including two GE and two Siemens scanners) on a group of healthy subjects. Diffusion measures such fractional anisotropy, mean diffusivity and generalized fractional anisotropy are compared across multiple sites before and after the mapping. Our experimental results demonstrate that, for identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.

From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure

A major goal of cognitive neuroscience is to delineate how brain systems give rise to mental function. Here we review the increasingly large role informatics-driven approaches are playing in such efforts. We begin by reviewing a number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings--for example, the difficulty in establishing the specificity of postulated associations. Next, we demonstrate how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis--including meta-analytic methods that synthesize hundreds or thousands of studies at a time; latent-variable approaches that seek to extract structure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively inferring mental states from patterns of brain activity. We highlight the underappreciated but critical role for formal cognitive ontologies in helping to clarify, refine, and test theories of brain and cognitive function. Finally, we conclude with a speculative discussion of what future informatics developments may hold for cognitive neuroscience.

Gray Matter Alterations in Post-Traumatic Stress Disorder, Obsessive-Compulsive Disorder, and Social Anxiety Disorder

Post-traumatic stress disorder (PTSD), obsessive–compulsive disorder (OCD), and social anxiety disorder (SAD) all bear the core symptom of anxiety and are separately classified in the new DSM-5 system. The aim of the present study is to obtain evidence for neuroanatomical difference for these disorders. We applied voxel-based morphometry (VBM) with Diffeomorphic Anatomical Registration Through Exponentiated Lie to compare gray matter volume (GMV) in magnetic resonance images obtained for 30 patients with PTSD, 29 patients with OCD, 20 patients with SAD, and 30 healthy controls. GMV across all four groups differed in left hypothalamus and left inferior parietal lobule and post hoc analyses revealed that this difference is primarily due to reduced GMV in the PTSD group relative to the other groups. Further analysis revealed that the PTSD group also showed reduced GMV in frontal lobe, temporal lobe, and cerebellum compared to the OCD group, and reduced GMV in frontal lobes bilaterally compared to SAD group. A significant negative correlation with anxiety symptoms is observed for GMV in left hypothalamus in three disorder groups. We have thus found evidence for brain structure differences that in future could provide biomarkers to potentially support classification of these disorders using MRI.

Meta-analytic connectivity and behavioral parcellation of the human cerebellum

The cerebellum historically has been thought to mediate motor and sensory signals between the body and cerebral cortex, yet cerebellar lesions are also associated with altered cognitive behavioral performance. Neuroimaging evidence indicates that the cerebellum contributes to a wide range of cognitive, perceptual, and motor functions. Here, we used the BrainMap database to investigate whole-brainco-activation patterns between cerebellar structures and regions of the cerebral cortex, as well as associations with behavioral tasks. Hierarchical clustering was performed to meta-analytically identify cerebellar structures with similar cortical co-activation, and independently, with similar correlations to specific behavioral tasks. Strong correspondences were observed in these separate but parallel analyses of meta-analytic connectivity and behavioral metadata. We recovered differential zones of cerebellar co-activation that are reflected across the literature. Furthermore, the behaviors and tasks associated with the different cerebellar zones provide insight into the specialized function of the cerebellum, relating to high-order cognition, emotion, perception, interoception, and action. Taken together, these task-basedmeta-analytic results implicate distinct zones of the cerebellum as critically involved in the monitoring and mediation of psychological responses to internal and external stimuli.

Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community

Accelerating insight into the relation between brain and behavior entails conducting small and large-scale research endeavors that lead to reproducible results. Consensus is emerging between funding agencies, publishers, and the research community that data sharing is a fundamental requirement to ensure all such endeavors foster data reuse and fuel reproducible discoveries. Funding agency and publisher mandates to share data are bolstered by a growing number of data sharing efforts that demonstrate how information technologies can enable meaningful data reuse. Neuroinformatics evaluates scientific needs and develops solutions to facilitate the use of data across the cognitive and neurosciences. For example, electronic data capture and management tools designed to facilitate human neurocognitive research can decrease the setup time of studies, improve quality control, and streamline the process of harmonizing, curating, and sharing data across data repositories. In this article we outline the advantages and disadvantages of adopting software applications that support these features by reviewing the tools available and then presenting two contrasting neuroimaging study scenarios in the context of conducting a cross-sectional and a multisite longitudinal study.

Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity

IMPORTANCE

Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive.

OBJECTIVE

To investigate network dysfunction in MDD through a meta-analysis of rsFC studies.

DATA SOURCES

Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data.

STUDY SELECTION

Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis.

DATA EXTRACTION AND SYNTHESIS

Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network.

RESULTS

Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions.

CONCLUSIONS AND RELEVANCE

Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.

NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain

NeuroVault.org is dedicated to storing outputs of analyses in the form of statistical maps, parcellations and atlases, a unique strategy that contrasts with most neuroimaging repositories that store raw acquisition data or stereotaxic coordinates. Such maps are indispensable for performing meta-analyses, validating novel methodology, and deciding on precise outlines for regions of interest (ROIs). NeuroVault is open to maps derived from both healthy and clinical populations, as well as from various imaging modalities (sMRI, fMRI, EEG, MEG, PET, etc.). The repository uses modern web technologies such as interactive web-based visualization, cognitive decoding, and comparison with other maps to provide researchers with efficient, intuitive tools to improve the understanding of their results. Each dataset and map is assigned a permanent Universal Resource Locator (URL), and all of the data is accessible through a REST Application Programming Interface (API). Additionally, the repository supports the NIDM-Results standard and has the ability to parse outputs from popular FSL and SPM software packages to automatically extract relevant metadata. This ease of use, modern web-integration, and pioneering functionality holds promise to improve the workflow for making inferences about and sharing whole-brain statistical maps.

NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain

Here we present NeuroVault—a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta- and coactivation analyses.

Gray matter atrophy in narcolepsy: An activation likelihood estimation meta-analysis

The authors reviewed the literature on the use of voxel-based morphometry (VBM) in narcolepsy magnetic resonance imaging (MRI) studies via the use of a meta-analysis of neuroimaging to identify concordant and specific structural deficits in patients with narcolepsy as compared with healthy subjects. We used PubMed to retrieve articles published between January 2000 and March 2014. The authors included all VBM research on narcolepsy and compared the findings of the studies by using gray matter volume (GMV) or gray matter concentration (GMC) to index differences in gray matter. Stereotactic data were extracted from 8 VBM studies of 149 narcoleptic patients and 162 control subjects. We applied activation likelihood estimation (ALE) technique and found significant regional gray matter reduction in the bilateral hypothalamus, thalamus, globus pallidus, extending to nucleus accumbens (NAcc) and anterior cingulate cortex (ACC), left mid orbital and rectal gyri (BAs 10 and 11), right inferior frontal gyrus (BA 47), and the right superior temporal gyrus (BA 41) in patients with narcolepsy. The significant gray matter deficits in narcoleptic patients occurred in the bilateral hypothalamus and frontotemporal regions, which may be related to the emotional processing abnormalities and orexin/hypocretin pathway common among populations of patients with narcolepsy.

Reliability of an fMRI paradigm for emotional processing in a multisite longitudinal study

Multisite neuroimaging studies can facilitate the investigation of brain-related changes in many contexts, including patient groups that are relatively rare in the general population. Though multisite studies have characterized the reliability of brain activation during working memory and motor functional magnetic resonance imaging tasks, emotion processing tasks, pertinent to many clinical populations, remain less explored. A traveling participants study was conducted with eight healthy volunteers scanned twice on consecutive days at each of the eight North American Longitudinal Prodrome Study sites. Tests derived from generalizability theory showed excellent reliability in the amygdala ( Eρ2 = 0.82), inferior frontal gyrus (IFG; Eρ2 = 0.83), anterior cingulate cortex (ACC; Eρ2 = 0.76), insula ( Eρ2 = 0.85), and fusiform gyrus ( Eρ2 = 0.91) for maximum activation and fair to excellent reliability in the amygdala ( Eρ2 = 0.44), IFG ( Eρ2 = 0.48), ACC ( Eρ2 = 0.55), insula ( Eρ2 = 0.42), and fusiform gyrus ( Eρ2 = 0.83) for mean activation across sites and test days. For the amygdala, habituation ( Eρ2 = 0.71) was more stable than mean activation. In a second investigation, data from 111 healthy individuals across sites were aggregated in a voxelwise, quantitative meta-analysis. When compared with a mixed effects model controlling for site, both approaches identified robust activation in regions consistent with expected results based on prior single-site research. Overall, regions central to emotion processing showed strong reliability in the traveling participants study and robust activation in the aggregation study. These results support the reliability of blood oxygen level-dependent signal in emotion processing areas across different sites and scanners and may inform future efforts to increase efficiency and enhance knowledge of rare conditions in the population through multisite neuroimaging paradigms.

The wandering brain: meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes

The neural basis and cognitive functions of various spontaneous thought processes, particularly mind-wandering, are increasingly being investigated. Although strong links have been drawn between the occurrence of spontaneous thought processes and activation in brain regions comprising the default mode network (DMN), spontaneous thought also appears to recruit other, non-DMN regions just as consistently. Here we present the first quantitative meta-analysis of neuroimaging studies of spontaneous thought and mind-wandering in order to address the question of their neural correlates. Examining 24 functional neuroimaging studies of spontaneous thought processes, we conducted a meta-analysis using activation likelihood estimation (ALE). A number of key DMN areas showed consistent recruitment across studies, including medial prefrontal cortex, posterior cingulate cortex, medial temporal lobe, and bilateral inferior parietal lobule. Numerous non-DMN regions, however, were also consistently recruited, including rostrolateral prefrontal cortex, dorsal anterior cingulate cortex, insula, temporopolar cortex, secondary somatosensory cortex, and lingual gyrus. These meta-analytic results indicate that DMN activation alone is insufficient to adequately capture the neural basis of spontaneous thought; frontoparietal control network areas, and other non-DMN regions, appear to be equally central. We conclude that further progress in the cognitive and clinical neuroscience of spontaneous thought will therefore require a re-balancing of our view of the contributions of various regions and networks throughout the brain, and beyond the DMN.

The Brain Basis of Positive and Negative Affect: Evidence from a Meta-Analysis of the Human Neuroimaging Literature

The ability to experience pleasant or unpleasant feelings or to represent objects as "positive" or "negative" is known as representing hedonic "valence." Although scientists overwhelmingly agree that valence is a basic psychological phenomenon, debate continues about how to best conceptualize it scientifically. We used a meta-analysis of 397 functional magnetic resonance imaging (fMRI) and positron emission tomography studies (containing 914 experimental contrasts and 6827 participants) to test 3 competing hypotheses about the brain basis of valence: the bipolarity hypothesis that positive and negative affect are supported by a brain system that monotonically increases and/or decreases along the valence dimension, the bivalent hypothesis that positive and negative affect are supported by independent brain systems, and the affective workspace hypothesis that positive and negative affect are supported by a flexible set of valence-general regions. We found little evidence for the bipolar or bivalent hypotheses. Findings instead supported the hypothesis that, at the level of brain activity measurable by fMRI, valence is flexibly implemented across instances by a set of valence-general limbic and paralimbic brain regions.

Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid analgesia

We present a new method, State-based Dynamic Community Structure, that detects time-dependent community structure in networks of brain regions. Most analyses of functional connectivity assume that network behavior is static in time, or differs between task conditions with known timing. Our goal is to determine whether brain network topology remains stationary over time, or if changes in network organization occur at unknown time points. Changes in network organization may be related to shifts in neurological state, such as those associated with learning, drug uptake or experimental conditions. Using a hidden Markov stochastic blockmodel, we define a time-dependent community structure. We apply this approach to data from a functional magnetic resonance imaging experiment examining how contextual factors influence drug-induced analgesia. Results reveal that networks involved in pain, working memory, and emotion show distinct profiles of time-varying connectivity.

Pain anticipation: an activation likelihood estimation meta-analysis of brain imaging studies

The anticipation of pain has been investigated in a variety of brain imaging studies. Importantly, today there is no clear overall picture of the areas that are involved in different studies and the exact role of these regions in pain expectation remains especially unexploited. To address this issue, we used activation likelihood estimation meta-analysis to analyze pain anticipation in several neuroimaging studies. A total of 19 functional magnetic resonance imaging were included in the analysis to search for the cortical areas involved in pain anticipation in human experimental models. During anticipation, activated foci were found in the dorsolateral prefrontal, midcingulate and anterior insula cortices, medial and inferior frontal gyri, inferior parietal lobule, middle and superior temporal gyrus, thalamus, and caudate. Deactivated foci were found in the anterior cingulate, superior frontal gyrus, parahippocampal gyrus and in the claustrum. The results of the meta-analytic connectivity analysis provide an overall view of the brain responses triggered by the anticipation of a noxious stimulus. Such a highly distributed perceptual set of self-regulation may prime brain regions to process information where emotion, action and perception as well as their related subcategories play a central role. Not only do these findings provide important information on the neural events when anticipating pain, but also they may give a perspective into nocebo responses, whereby negative expectations may lead to pain worsening.

A review of feature reduction techniques in neuroimaging

Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.

Anatomical likelihood estimation meta-analysis of grey and white matter anomalies in autism spectrum disorders

Autism spectrum disorders (ASD) are characterized by impairments in social communication and restrictive, repetitive behaviors. While behavioral symptoms are well-documented, investigations into the neurobiological underpinnings of ASD have not resulted in firm biomarkers. Variability in findings across structural neuroimaging studies has contributed to difficulty in reliably characterizing the brain morphology of individuals with ASD. These inconsistencies may also arise from the heterogeneity of ASD, and wider age-range of participants included in MRI studies and in previous meta-analyses. To address this, the current study used coordinate-based anatomical likelihood estimation (ALE) analysis of 21 voxel-based morphometry (VBM) studies examining high-functioning individuals with ASD, resulting in a meta-analysis of 1055 participants (506 ASD, and 549 typically developing individuals). Results consisted of grey, white, and global differences in cortical matter between the groups. Modeled anatomical maps consisting of concentration, thickness, and volume metrics of grey and white matter revealed clusters suggesting age-related decreases in grey and white matter in parietal and inferior temporal regions of the brain in ASD, and age-related increases in grey matter in frontal and anterior-temporal regions. White matter alterations included fiber tracts thought to play key roles in information processing and sensory integration. Many current theories of pathobiology ASD suggest that the brains of individuals with ASD may have less-functional long-range (anterior-to-posterior) connections. Our findings of decreased cortical matter in parietal-temporal and occipital regions, and thickening in frontal cortices in older adults with ASD may entail altered cortical anatomy, and neurodevelopmental adaptations.

Reading the dyslexic brain: multiple dysfunctional routes revealed by a new meta-analysis of PET and fMRI activation studies

Developmental dyslexia has been the focus of much functional anatomical research. The main trust of this work is that typical developmental dyslexics have a dysfunction of the phonological and orthography to phonology conversion systems, in which the left occipito-temporal cortex has a crucial role. It remains to be seen whether there is a systematic co-occurrence of dysfunctional patterns of different functional systems perhaps converging on the same brain regions associated with the reading deficit. Such evidence would be relevant for theories like, for example, the magnocellular/attentional or the motor/cerebellar ones, which postulate a more basic and anatomically distributed disorder in dyslexia. We addressed this issue with a meta-analysis of all the imaging literature published until September 2013 using a combination of hierarchical clustering and activation likelihood estimation methods. The clustering analysis on 2360 peaks identified 193 clusters, 92 of which proved spatially significant. Following binomial tests on the clusters, we found left hemispheric network specific for normal controls (i.e., of reduced involvement in dyslexics) including the left inferior frontal, premotor, supramarginal cortices and the left infero-temporal and fusiform regions: these were preferentially associated with reading and the visual-to-phonology processes. There was also a more dorsal left fronto-parietal network: these clusters included peaks from tasks involving phonological manipulation, but also motoric or visuo-spatial perception/attention. No cluster was identified in area V5 for no task, nor cerebellar clusters showed a reduced association with dyslexics. We conclude that the examined literature demonstrates a specific lack of activation of the left occipito-temporal cortex in dyslexia particularly for reading and reading-like behaviors and for visuo-phonological tasks. Additional deficits of motor and attentional systems relevant for reading may be associated with altered functionality of dorsal left fronto-parietal cortex.

Reliability of functional magnetic resonance imaging activation during working memory in a multi-site study: analysis from the North American Prodrome Longitudinal Study

Multi-site neuroimaging studies offer an efficient means to study brain functioning in large samples of individuals with rare conditions; however, they present new challenges given that aggregating data across sites introduces additional variability into measures of interest. Assessing the reliability of brain activation across study sites and comparing statistical methods for pooling functional data are critical to ensuring the validity of aggregating data across sites. The current study used two samples of healthy individuals to assess the feasibility and reliability of aggregating multi-site functional magnetic resonance imaging (fMRI) data from a Sternberg-style verbal working memory task. Participants were recruited as part of the North American Prodrome Longitudinal Study (NAPLS), which comprises eight fMRI scanning sites across the United States and Canada. In the first study sample (n=8), one participant from each home site traveled to each of the sites and was scanned while completing the task on two consecutive days. Reliability was examined using generalizability theory. Results indicated that blood oxygen level-dependent (BOLD) signal was reproducible across sites and was highly reliable, or generalizable, across scanning sites and testing days for core working memory ROIs (generalizability ICCs=0.81 for left dorsolateral prefrontal cortex, 0.95 for left superior parietal cortex). In the second study sample (n=154), two statistical methods for aggregating fMRI data across sites for all healthy individuals recruited as control participants in the NAPLS study were compared. Control participants were scanned on one occasion at the site from which they were recruited. Results from the image-based meta-analysis (IBMA) method and mixed effects model with site covariance method both showed robust activation in expected regions (i.e. dorsolateral prefrontal cortex, anterior cingulate cortex, supplementary motor cortex, superior parietal cortex, inferior temporal cortex, cerebellum, thalamus, basal ganglia). Quantification of the similarity of group maps from these methods confirmed a very high (96%) degree of spatial overlap in results. Thus, brain activation during working memory function was reliable across the NAPLS sites and both the IBMA and mixed effects model with site covariance methods appear to be valid approaches for aggregating data across sites. These findings indicate that multi-site functional neuroimaging can offer a reliable means to increase power and generalizability of results when investigating brain function in rare populations and support the multi-site investigation of working memory function in the NAPLS study, in particular.

Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners

Numerous studies have begun to address how the brain's gray and white matter may be shaped by meditation. This research is yet to be integrated, however, and two fundamental questions remain: Is meditation associated with altered brain structure? If so, what is the magnitude of these differences? To address these questions, we reviewed and meta-analyzed 123 brain morphology differences from 21 neuroimaging studies examining ∼300 meditation practitioners. Anatomical likelihood estimation (ALE) meta-analysis found eight brain regions consistently altered in meditators, including areas key to meta-awareness (frontopolar cortex/BA 10), exteroceptive and interoceptive body awareness (sensory cortices and insula), memory consolidation and reconsolidation (hippocampus), self and emotion regulation (anterior and mid cingulate; orbitofrontal cortex), and intra- and interhemispheric communication (superior longitudinal fasciculus; corpus callosum). Effect size meta-analysis (calculating 132 effect sizes from 16 studies) suggests a global 'medium' effect size (Cohen's d¯=0.46; r¯=.19). Publication bias and methodological limitations are strong concerns, however. Further research using rigorous methods is required to definitively link meditation practice to altered brain morphology.