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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.

•We assessed the ability of deep networks heatmaps to capture AD effects on the brain.•SVMs, CNNs and ResNets were trained to classify 502 ADNI T1 MRIs.•GGC, integrated gradient and LRP used to produce heatmaps for the best classifiers.•Heatmaps were compared with a meta-analysis of 77 VBM studies and SVM activations.•Integrated gradient heatmaps produced the best overlap with the meta-analysis. Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer’s disease and healthy controls. Unfortunately, the subtle brain alterations captured by these models are difficult to interpret because of the complexity of these multi-layer and non-linear models. Several heatmap methods have been proposed to address this issue and analyze the imaging patterns extracted from the deep neural networks, but no quantitative comparison between these methods has been carried out so far. In this work, we explore these questions by deriving heatmaps from Convolutional Neural Networks (CNN) trained using T1 MRI scans of the ADNI data set and by comparing these heatmaps with brain maps corresponding to Support Vector Machine (SVM) activation patterns. Three prominent heatmap methods are studied: Layer-wise Relevance Propagation (LRP), Integrated Gradients (IG), and Guided Grad-CAM (GGC). Contrary to prior studies where the quality of heatmaps was visually or qualitatively assessed, we obtained precise quantitative measures by computing overlap with a ground-truth map from a large meta-analysis that combined 77 voxel-based morphometry (VBM) studies independently from ADNI. Our results indicate that all three heatmap methods were able to capture brain regions covering the meta-analysis map and achieved better results than SVM activation patterns. Among them, IG produced the heatmaps with the best overlap with the independent meta-analysis.

•ALFF spatially correlates significantly only with BV.•Conditioned by BV, ALFF contains no other physiological information.•fALFF and ReHo spatially correlate most strongly with MRGlu, but also with MRO2,BF and BV.•Conditioned by MRGlu, fALFF and ReHo contain little physiological information. Voxel-based physiological (VBP) variables derived from blood oxygen level dependent (BOLD) fMRI time-course variations include: amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Although these BOLD-derived variables can detect between-group (e.g. disease vs control) spatial pattern differences, physiological interpretations are not well established. The primary objective of this study was to quantify spatial correspondences between BOLD VBP variables and PET measurements of cerebral metabolic rate and hemodynamics, being well-validated physiological standards. To this end, quantitative, whole-brain PET images of metabolic rate of glucose (MRGlu; 18FDG) and oxygen (MRO2; 15OO), blood flow (BF; H215O) and blood volume (BV; C15O) were obtained in 16 healthy controls. In the same subjects, BOLD time-courses were obtained for computation of ALFF, fALFF and ReHo images. PET variables were compared pair-wise with BOLD variables. In group-averaged, across-region analyses, ALFF corresponded significantly only with BV (R = 0.64; p < 0.0001). fALFF corresponded most strongly with MRGlu (R = 0.79; p < 0.0001), but also significantly (p < 0.0001) with MRO2 (R = 0.68), BF (R = 0.68) and BV (R=0.68). ReHo performed similarly to fALFF, with significant strong correspondence (p < 0.0001) with MRGlu (R = 0.78), MRO2 (R = 0.54), and, but less strongly with BF (R = 0.50) and BV (R=0.50). Mutual information analyses further clarified these physiological interpretations. When conditioned by BV, ALFF retained no significant MRGlu, MRO2 or BF information. When conditioned by MRGlu, fALFF and ReHo retained no significant MRO2, BF or BV information. Of concern, however, the strength of PET-BOLD correspondences varied markedly by brain region, which calls for future investigation on physiological interpretations at a regional and per-subject basis.

Stress is present in everyday life in various forms and situations. Two stressors frequently investigated are physiological and psychosocial stress. Besides similar subjective and hormonal responses, it has been suggested that they also share common neural substrates. The current study used activation-likelihood-estimation meta-analysis to test this assumption by integrating results of previous neuroimaging studies on stress processing. Reported results are cluster-level FWE corrected. The inferior frontal gyrus (IFG) and the anterior insula (AI) were the only regions that demonstrated overlapping activation for both stressors. Analysis of physiological stress showed consistent activation of cognitive and affective components of pain processing such as the insula, striatum, or the middle cingulate cortex. Contrarily, analysis across psychosocial stress revealed consistent activation of the right superior temporal gyrus and deactivation of the striatum. Notably, parts of the striatum appeared to be functionally specified: the dorsal striatum was activated in physiological stress, whereas the ventral striatum was deactivated in psychosocial stress. Additional functional connectivity and decoding analyses further characterized this functional heterogeneity and revealed higher associations of the dorsal striatum with motor regions and of the ventral striatum with reward processing. Based on our meta-analytic approach, activation of the IFG and the AI seems to indicate a global neural stress reaction. While physiological stress activates a motoric fight-or-flight reaction, during psychosocial stress attention is shifted towards emotion regulation and goal-directed behavior, and reward processing is reduced. Our results show the significance of differentiating physiological and psychosocial stress in neural engagement. Furthermore, the assessment of deactivations in addition to activations in stress research is highly recommended. •We meta-analytically analyzed physiological and psychosocial stress.•IFG and insula are convergently activated in physiological and psychosocial stress.•Physiological stress activates regions included in pain processing.•Psychosocial stress activates the right STG and deactivates the left striatum.•Dorsal and ventral striatum differ functionally in stress processing.

Cytoarchitectonic area 44 of Broca's region in the left inferior frontal gyrus is known to be involved in several functional domains including language, action and music processing. We investigated whether this functional heterogeneity is reflected in distinct modules within cytoarchitectonically defined left area 44 using meta-analytic connectivity-based parcellation (CBP). This method relies on identifying the whole-brain co-activation pattern for each area 44 voxel across a wide range of functional neuroimaging experiments and subsequently grouping the voxels into distinct clusters based on the similarity of their co-activation patterns. This CBP analysis revealed that five separate clusters exist within left area 44. A post-hoc functional characterization and functional connectivity analysis of these five clusters was then performed. The two posterior clusters were primarily associated with action processes, in particular with phonology and overt speech (posterior-dorsal cluster) and with rhythmic sequencing (posterior-ventral cluster). The three anterior clusters were primarily associated with language and cognition, in particular with working memory (anterior-dorsal cluster), with detection of meaning (anterior-ventral cluster) and with task switching/cognitive control (inferior frontal junction cluster). These five clusters furthermore showed specific and distinct connectivity patterns. The results demonstrate that left area 44 is heterogeneous, thus supporting anatomical data on the molecular architecture of this region, and provide a basis for more specific interpretations of activations localized in area 44. •Left area 44 of Broca's region is functionally heterogeneous.•Parcellation revealed five distinct clusters based on co-activation pattern.•Posterior clusters more linked with action (overt speech/rhythmic sequencing).•Anterior clusters more linked with language (semantics and meaning/working memory).•Inferior frontal junction cluster linked with cognitive control.

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is \"at rest.\" In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically \"active\" even when at \"rest.\"

Recent evidence suggests considerable overlap between the default mode network (DMN) and regions involved in social, affective and introspective processes. We considered these overlapping regions as the social-affective part of the DMN. In this study, we established a robust mapping of the underlying brain network formed by these regions and those strongly connected to them (the extended social-affective default network). We first seeded meta-analytic connectivity modeling and resting-state analyses in the meta-analytically defined DMN regions that showed statistical overlap with regions associated with social and affective processing. Consensus connectivity of each seed was subsequently delineated by a conjunction across both connectivity analyses. We then functionally characterized the ensuing regions and performed several cluster analyses. Among the identified regions, the amygdala/hippocampus formed a cluster associated with emotional processes and memory functions. The ventral striatum, anterior cingulum, subgenual cingulum and ventromedial prefrontal cortex formed a heterogeneous subgroup associated with motivation, reward and cognitive modulation of affect. Posterior cingulum/precuneus and dorsomedial prefrontal cortex were associated with mentalizing, self-reference and autobiographic information. The cluster formed by the temporo-parietal junction and anterior middle temporal sulcus/gyrus was associated with language and social cognition. Taken together, the current work highlights a robustly interconnected network that may be central to introspective, socio-affective, that is, self- and other-related mental processes.

The posterior medial cortex (PMC) is particularly poorly understood. Its neural activity changes have been related to highly disparate mental processes. We therefore investigated PMC properties with a data-driven exploratory approach. First, we subdivided the PMC by whole-brain coactivation profiles. Second, functional connectivity of the ensuing PMC regions was compared by task-constrained meta-analytic coactivation mapping (MACM) and task-unconstrained resting-state correlations (RSFC). Third, PMC regions were functionally described by forward/reverse functional inference. A precuneal cluster was mostly connected to the intraparietal sulcus, frontal eye fields, and right temporo-parietal junction; associated with attention and motor tasks. A ventral posterior cingulate cortex (PCC) cluster was mostly connected to the ventromedial prefrontal cortex and middle left inferior parietal cortex (IPC); associated with facial appraisal and language tasks. A dorsal PCC cluster was mostly connected to the dorsomedial prefrontal cortex, anterior/posterior IPC, posterior midcingulate cortex, and left dorsolateral prefrontal cortex; associated with delay discounting. A cluster in the retrosplenial cortex was mostly connected to the anterior thalamus and hippocampus. Furthermore, all PMC clusters were congruently coupled with the default mode network according to task-unconstrained but not task-constrained connectivity. We thus identified distinct regions in the PMC and characterized their neural networks and functional implications. •Connectivity-based parcellation identified four distinct cortical modules.•The clusters related to processing attention, perspectives, object and space facets.•All clusters were connected to the default-mode network at rest, but not during task.

Psychopathy is a disorder of high public concern because it predicts violence and offense recidivism. Recent brain imaging studies suggest abnormal brain activity underlying psychopathic behavior. No reliable pattern of altered neural activity has been disclosed so far. This study sought to identify consistent changes of brain activity in psychopaths and to investigate whether these could explain known psychopathology. First, we used activation likelihood estimation (p < 0.05, corrected) to meta-analyze brain activation changes associated with psychopathy across 28 functional magnetic resonance imaging studies reporting 753 foci from 155 experiments. Second, we characterized the ensuing regions functionally by employing metadata of a large-scale neuroimaging database (p < 0.05, corrected). Psychopathy was consistently associated with decreased brain activity in the right laterobasal amygdala, the dorsomedial prefrontal cortex, and bilaterally in the lateral prefrontal cortex. A robust increase of activity was observed in the fronto-insular cortex on both hemispheres. Data-driven functional characterization revealed associations with semantic language processing (left lateral prefrontal and fronto-insular cortex), action execution and pain processing (right lateral prefrontal and left fronto-insular), social cognition (dorsomedial prefrontal cortex), and emotional as well as cognitive reward processing (right amygdala and fronto-insular cortex). Aberrant brain activity related to psychopathy is located in prefrontal, insular, and limbic regions. Physiological mental functions fulfilled by these brain regions correspond to disturbed behavioral patterns pathognomonic for psychopathy. Hence, aberrant brain activity may not just be an epiphenomenon of psychopathy but directly related to the psychopathology of this disorder.