Activation Likelihood Estimation Maps Research Articles

The neural substrates responsible for food odor processing: an activation likelihood estimation meta-analysis.

In many species including humans, food odors appear to play a distinct role when compared with other odors. Despite their functional distinction, the neural substrates responsible for food odor processing remain unclear in humans. This study aimed to identify brain regions involved in food odor processing using activation likelihood estimation (ALE) meta-analysis. We selected olfactory neuroimaging studies conducted with sufficient methodological validity using pleasant odors. We then divided the studies into food and non-food odor conditions. Finally, we performed an ALE meta-analysis for each category and compared the ALE maps of the two categories to identify the neural substrates responsible for food odor processing after minimizing the confounding factor of odor pleasantness. The resultant ALE maps revealed that early olfactory areas are more extensively activated by food than non-food odors. Subsequent contrast analysis identified a cluster in the left putamen as the most likely neural substrate underlying food odor processing. In conclusion, food odor processing is characterized by the functional network involved in olfactory sensorimotor transformation for approaching behaviors to edible odors, such as active sniffing.

A Minimum Bayes Factor Based Threshold for Activation Likelihood Estimation

Activation likelihood estimation (ALE) is among the most used algorithms to perform neuroimaging meta-analysis. Since its first implementation, several thresholding procedures had been proposed, all referred to the frequentist framework, returning a rejection criterion for the null hypothesis according to the critical p-value selected. However, this is not informative in terms of probabilities of the validity of the hypotheses. Here, we describe an innovative thresholding procedure based on the concept of minimum Bayes factor (mBF). The use of the Bayesian framework allows to consider different levels of probability, each of these being equally significant. In order to simplify the translation between the common ALE practice and the proposed approach, we analised six task-fMRI/VBM datasets and determined the mBF values equivalent to the currently recommended frequentist thresholds based on Family Wise Error (FWE). Sensitivity and robustness toward spurious findings were also analyzed. Results showed that the cutoff log10(mBF) = 5 is equivalent to the FWE threshold, often referred as voxel-level threshold, while the cutoff log10(mBF) = 2 is equivalent to the cluster-level FWE (c-FWE) threshold. However, only in the latter case voxels spatially far from the blobs of effect in the c-FWE ALE map survived. Therefore, when using the Bayesian thresholding the cutoff log10(mBF) = 5 should be preferred. However, being in the Bayesian framework, lower values are all equally significant, while suggesting weaker level of force for that hypothesis. Hence, results obtained through less conservative thresholds can be legitimately discussed without losing statistical rigor. The proposed technique adds therefore a powerful tool to the human-brain-mapping field.

Brain networks under uncertainty: A coordinate-based meta-analysis of brain imaging studies

In recent years, uncertainty has been extensively studied as a core factor in anxiety models. However, it remains unclear whether there is a stable brain circuitry to cope with uncertainty. Addressing this yet open question, we first distinguish uncertainty into three different states: risky, ambiguity, and threat anticipation. Then, we performed three meta-analyses of fMRI studies to identify those regions that are commonly activated by the three domains using activation likelihood estimation (ALE). The overlapping analyses of the three ALE maps revealed major conjunctions of the risk decision making, ambiguity decision making, and the threat anticipation in specifically the right insula. Contrast analysis further confirmed this finding. In addition, different uncertainty states also have different brain networks involved. Specifically, a large number of brain regions in the frontal-parietal cortex were recruited under ambiguity state, while subcortical gray matter regions were recruited under risk decision making, and the bilateral insula were closely associated with threat anticipation. Additionally, we assessed the co-activation pattern of identified regions using meta-analytic connectivity modeling (MACM) to investigate the potential network underlying the relationship of three domains. The MACM analysis further confirmed that different uncertain states have specific brain network basis. We concluded that the right insula serves as a convergent brain region for brain regions recruited for different uncertain states, and its co-activation pattern also corresponds to the brain network of the three uncertain states. This study is a preliminary attempt to further uncover the brain circuitry of anxiety models with uncertainty at their core.

Tasks activating the default mode network map multiple functional systems

Recent developments in network neuroscience suggest reconsidering what we thought we knew about the default mode network (DMN). Although this network has always been seen as unitary and associated with the resting state, a new deconstructive line of research is pointing out that the DMN could be divided into multiple subsystems supporting different functions. By now, it is well known that the DMN is not only deactivated by tasks, but also involved in affective, mnestic, and social paradigms, among others. Nonetheless, it is starting to become clear that the array of activities in which it is involved, might also be extended to more extrinsic functions. The present meta-analytic study is meant to push this boundary a bit further. The BrainMap database was searched for all experimental paradigms activating the DMN, and their activation likelihood estimation maps were then computed. An additional map of task-induced deactivations was also created. A multidimensional scaling indicated that such maps could be arranged along an anatomo-psychological gradient, which goes from midline core activations, associated with the most internal functions, to that of lateral cortices, involved in more external tasks. Further multivariate investigations suggested that such extrinsic mode is especially related to reward, semantic, and emotional functions. However, an important finding was that the various activation maps were often different from the canonical representation of the resting-state DMN, sometimes overlapping with it only in some peripheral nodes, and including external regions such as the insula. Altogether, our findings suggest that the intrinsic–extrinsic opposition may be better understood in the form of a continuous scale, rather than a dichotomy.

Sex difference in cerebral blood flow and cerebral glucose metabolism: an activation-likelihood estimation meta-analysis.

Sex differences exist in a variety of aspects including neurochemicals as well as behavioral traits of cognition, language, and aggression. We performed a meta-analysis of studies using a coordinate-based technique of activation-likelihood estimation (ALE) to identify the pooled estimated effect of sex difference. We performed a systematic search of MEDLINE and EMBASE for English-language publications using the keywords of 'positron emission tomography (PET)', 'single-photon emission computed tomography (SPECT)', and 'sex'. A threshold of uncorrected P < 0.001 (minimum volume of 200 mm3) was applied to the resulting ALE map. Cerebral blood flow (CBF) in right precuneus, left superior temporal gyrus, left inferior temporal, left inferior frontal gyrus, right cerebellar tonsil, and right middle temporal gyrus was higher in females than males. CBF in left anterior cingulate was higher in males than females. Whereas, the cerebral metabolic rate for glucose (CMRglu) in left thalamus, left cingulate gyrus, right inferior parietal lobule, left medial frontal gyrus, right middle frontal gyrus, right midbrain, and left inferior parietal lobule was higher in females than males. However, there was no brain region that showed higher CMRglu in males than females. Regional CBF and CMRglu from PET and SPECT showed the difference between males and females.

Cerebral glucose metabolism and Cerebral blood flow in thyroid dysfunction: An Activation Likelihood Estimation Meta-analysis

Thyroid dysfunction is frequently associated with functional disturbances of the brain. We performed a meta-analysis of previous positron emission tomography and single-photon emission computed tomography studies using a coordinate-based technique of activation-likelihood estimation (ALE) to investigate the potential background of neuropsychiatric complications in patients with hypo- and hyperthyroidism. We performed a systematic search of MEDLINE and EMBASE for English-language publications using the keywords of “positron emission tomography”, “single-photon emission computed tomography”, and “thyroid”. The software GingerALE ver 2.3.6 was used to transform all reported coordinates into stereotactic Montreal Neurological Institute space. A threshold of uncorrected p < 0.001 (minimum volume of 200 mm3) was applied to the resulting ALE map using cerebral metabolic rate of glucose (CMRglu), and cerebral blood flow (CBF). Six studies were eligible for inclusion in the study; 4 studies of cerebral metabolic rate of CMRglu, and 2 studies of CBF. In hypothyroidism, significant decreases in CMRglu were identified in 3 clusters including left anterior cingulate, right inferior occipital gyrus, and right cuneus. In hyperthyroidism, a significant decrease in CMRglu was identified in right superior frontal gyrus. In hypothyroidism, a significant decrease in CBF was observed in left postcentral gyrus. In conclusion, several brain regions showed altered CMRglu and CBF in patients with thyroid dysfunction compared with euthyroid controls. These findings might account for underlying mechanisms of thyroid hormones on psychological and physiological effects on brain.

Brain activation during the voiding phase of micturition in healthy adults: A meta-analysis of neuroimaging studies.

Several studies have used a variety of neuroimaging techniques to measure brain activity during the voiding phase of micturition. However, there is a lack of consensus on which regions of the brain are activated during voiding. The aim of this meta-analysis is to identify the brain regions that are consistently activated during voiding in healthy adults across different studies. We searched the literature for neuroimaging studies that reported brain co-ordinates that were activated during voiding. We excluded studies that reported co-ordinates only for bladder filling, during pelvic floor contraction only, and studies that focused on abnormal bladder states such as the neurogenic bladder. We used the activation-likelihood estimation (ALE) approach to create a statistical map of the brain and identify the brain co-ordinates that were activated across different studies. We identified nine studies that reported brain activation during the task of voiding in 91 healthy subjects. Together, these studies reported 117 foci for ALE analysis. Our ALE map yielded six clusters of activation in the pons, cerebellum, insula, anterior cingulate cortex (ACC), thalamus, and the inferior frontal gyrus. Regions of the brain involved in executive control (frontal cortex), interoception (ACC, insula), motor control (cerebellum, thalamus), and brainstem (pons) are involved in micturition. This analysis provides insight into the supraspinal control of voiding in healthy adults and provides a framework to understand dysfunctional voiding. Clin. Anat., 2018. © 2018 Wiley Periodicals, Inc.

An Activation Likelihood Estimation Meta-Analysis Study of Simple Motor Movements in Older and Young Adults.

The functional neuroanatomy of finger movements has been characterized with neuroimaging in young adults. However, less is known about the aging motor system. Several studies have contrasted movement-related activity in older versus young adults, but there is inconsistency among their findings. To address this, we conducted an activation likelihood estimation (ALE) meta-analysis on within-group data from older adults and young adults performing regularly paced right-hand finger movement tasks in response to external stimuli. We hypothesized that older adults would show a greater likelihood of activation in right cortical motor areas (i.e., ipsilateral to the side of movement) compared to young adults. ALE maps were examined for conjunction and between-group differences. Older adults showed overlapping likelihoods of activation with young adults in left primary sensorimotor cortex (SM1), bilateral supplementary motor area, bilateral insula, left thalamus, and right anterior cerebellum. Their ALE map differed from that of the young adults in right SM1 (extending into dorsal premotor cortex), right supramarginal gyrus, medial premotor cortex, and right posterior cerebellum. The finding that older adults uniquely use ipsilateral regions for right-hand finger movements and show age-dependent modulations in regions recruited by both age groups provides a foundation by which to understand age-related motor decline and motor disorders.

Mapping the "What" and "Where" Visual Cortices and Their Atrophy in Alzheimer's Disease: Combined Activation Likelihood Estimation with Voxel-Based Morphometry.

The human cortical regions for processing high-level visual (HLV) functions of different categories remain ambiguous, especially in terms of their conjunctions and specifications. Moreover, the neurobiology of declined HLV functions in patients with Alzheimer's disease (AD) has not been fully investigated. This study provides a functionally sorted overview of HLV cortices for processing “what” and “where” visual perceptions and it investigates their atrophy in AD and MCI patients. Based upon activation likelihood estimation (ALE), brain regions responsible for processing five categories of visual perceptions included in “what” and “where” visions (i.e., object, face, word, motion, and spatial visions) were analyzed, and subsequent contrast analyses were performed to show regions with conjunctive and specific activations for processing these visual functions. Next, based on the resulting ALE maps, the atrophy of HLV cortices in AD and MCI patients was evaluated using voxel-based morphometry. Our ALE results showed brain regions for processing visual perception across the five categories, as well as areas of conjunction and specification. Our comparisons of gray matter (GM) volume demonstrated atrophy of three “where” visual cortices in late MCI group and extensive atrophy of HLV cortices (25 regions in both “what” and “where” visual cortices) in AD group. In addition, the GM volume of atrophied visual cortices in AD and MCI subjects was found to be correlated to the deterioration of overall cognitive status and to the cognitive performances related to memory, execution, and object recognition functions. In summary, these findings may add to our understanding of HLV network organization and of the evolution of visual perceptual dysfunction in AD as the disease progresses.

Affective mapping: An activation likelihood estimation (ALE) meta-analysis

Functional neuroimaging has the spatial resolution to explain the neural basis of emotions. Activation likelihood estimation (ALE), as opposed to traditional qualitative meta-analysis, quantifies convergence of activation across studies within affective categories. Others have used ALE to investigate a broad range of emotions, but without the convenience of the BrainMap database. We used the BrainMap database and analysis resources to run separate meta-analyses on coordinates reported for anger, anxiety, disgust, fear, happiness, humor, and sadness. Resultant ALE maps were compared to determine areas of convergence between emotions, as well as to identify affect-specific networks. Five out of the seven emotions demonstrated consistent activation within the amygdala, whereas all emotions consistently activated the right inferior frontal gyrus, which has been implicated as an integration hub for affective and cognitive processes. These data provide the framework for models of affect-specific networks, as well as emotional processing hubs, which can be used for future studies of functional or effective connectivity.

Different patterns of brain activation in normal aging and Alzheimer's disease from cognitional sight: meta analysis using activation likelihood estimation

Alzheimer disease (AD) is a chronic neurological disease, frequently affecting cognitional functions. Recently, a large body of neuro-imaging studies have aimed at finding reliable biomarkers of AD for early diagnosis of disease in contrast with healthy elderlies. We intended to have a meta-analytical study on recent functional neuroimaging studies to find the relationship between cognition in AD patients and normal elderlies.A systematic search was conducted to collect functional neuroimaging studies such as positron emission therapy (PET) and functional magnetic resonance imaging (fMRI) in AD patients and healthy elderlies. The coordinates of regions related to cognition were meta-analyzed using the activation likelihood estimation (ALE) method and Sleuth software. P-value map at the false discovery rate (FDR) of P<0.05 thresholds and the clusters with a minimum size of 200mm3 were considered. Data were visualized with MANGO software.Forty-one articles that explored the areas activated during cognition in normal elderly subjects and AD patients were found. According to the findings, left middle frontal gyrus and left precuneus are the most activated areas in cognitional tasks in healthy elderlies and AD patients respectively. In normal elderly subjects and AD patients, comparison of ALE maps and reverse contrast showed that insula and left precuneus were the most activated areas in cognitional aspects respectively. With respect to unification of left precuneus activation in cognitional tasks, it seems that this point can be a hallmark in primary differentiation of AD and healthy individuals.

Enhanced visual functioning in autism: An ALE meta‐analysis

Autistics often exhibit enhanced perceptual abilities when engaged in visual search, visual discrimination, and embedded figure detection. In similar fashion, while performing a range of perceptual or cognitive tasks, autistics display stronger physiological engagement of the visual system than do non-autistics. To account for these findings, the Enhanced Perceptual Functioning Model proposes that enhanced autistic performance in basic perceptual tasks results from stronger engagement of sensory processing mechanisms, a situation that may facilitate an atypically prominent role for perceptual mechanisms in supporting cognition. Using quantitative meta-analysis of published functional imaging studies from which Activation Likelihood Estimation maps were computed, we asked whether autism is associated with enhanced task-related activity for a broad range of visual tasks. To determine whether atypical engagement of visual processing is a general or domain-specific phenomenon, we examined three different visual processing domains: faces, objects, and words. Overall, we observed more activity in autistics compared to non-autistics in temporal, occipital, and parietal regions. In contrast, autistics exhibited less activity in frontal cortex. The spatial distribution of the observed differential between-group patterns varied across processing domains. Autism may be characterized by enhanced functional resource allocation in regions associated with visual processing and expertise. Atypical adult organizational patterns may reflect underlying differences in developmental neural plasticity that can result in aspects of the autistic phenotype, including enhanced visual skills, atypical face processing, and hyperlexia.

Brain regions involved in human movement perception: A quantitative voxel‐based meta‐analysis

Face, hands, and body movements are powerful signals essential for social interactions. In the last 2 decades, a large number of brain imaging studies have explored the neural correlates of the perception of these signals. Formal synthesis is crucially needed, however, to extract the key circuits involved in human motion perception across the variety of paradigms and stimuli that have been used. Here, we used the activation likelihood estimation (ALE) meta-analysis approach with random effect analysis. We performed meta-analyses on three classes of biological motion: movement of the whole body, hands, and face. Additional analyses of studies of static faces or body stimuli and sub-analyses grouping experiments as a function of their control stimuli or task employed allowed us to identify main effects of movements and forms perception, as well as effects of task demand. In addition to specific features, all conditions showed convergence in occipito-temporal and fronto-parietal regions, but with different peak location and extent. The conjunction of the three ALE maps revealed convergence in all categories in a region of the right posterior superior temporal sulcus as well as in a bilateral region at the junction between middle temporal and lateral occipital gyri. Activation in these regions was not a function of attentional demand and was significant also when controlling for non-specific motion perception. This quantitative synthesis points towards a special role for posterior superior temporal sulcus for integrating human movement percept, and supports a specific representation for body parts in middle temporal, fusiform, precentral, and parietal areas.

Minimizing within‐experiment and within‐group effects in activation likelihood estimation meta‐analyses

Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate-based meta-analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within-experiment effects. However, we show that these effects only account for 2-3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7-9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within-experiment or within-group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within-experiment and within-group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports.