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IntroductionHumans use discriminative touch to perceive texture through dynamic interactions with surfaces, activating low-threshold mechanoreceptors in the skin. It was largely assumed that texture was processed in primary somatosensory regions in the brain; however, imaging studies indicate heterogeneous patterns of brain activity associated with texture processing.MethodsTo address this, we conducted a coordinate-based activation likelihood estimation meta-analysis of 13 functional magnetic resonance imaging studies (comprising 15 experiments contributing 228 participants and 275 foci) selected by a systematic review.ResultsConcordant activations for texture perception occurred in the left primary somatosensory and motor regions, with bilateral activations in the secondary somatosensory, posterior insula, and premotor and supplementary motor cortices. We also evaluated differences between studies that compared touch processing to non-haptic control (e.g., rest or visual control) or those that used haptic control (e.g., shape or orientation perception) to specifically investigate texture encoding. Studies employing a haptic control revealed concordance for texture processing only in the left secondary somatosensory cortex. Contrast analyses demonstrated greater concordance of activations in the left primary somatosensory regions and inferior parietal cortex for studies with a non-haptic control, compared to experiments accounting for other haptic aspects.ConclusionThese findings suggest that texture processing may recruit higher order integrative structures, and the secondary somatosensory cortex may play a key role in encoding textural properties. The present study provides unique insight into the neural correlates of texture-related processing by assessing the influence of non-textural haptic elements and identifies opportunities for a future research design to understand the neural processing of texture.

Objective: The objective of this study is to summarize and analyze the brain signal patterns of empathy for pain caused by facial expressions of pain utilizing activation likelihood estimation, a meta-analysis method. Data Sources: Studies concerning the brain mechanism were searched from the Science Citation Index, Science Direct, PubMed, DeepDyve, Cochrane Library, SinoMed, Wanfang, VIP, China National Knowledge Infrastructure, and other databases, such as SpringerLink, AMA, Science Online, Wiley Online, were collected. A time limitation of up to 13 December 2016 was applied to this study. Data Selection: Studies presenting with all of the following criteria were considered for study inclusion: Use of functional magnetic resonance imaging, neutral and pained facial expression stimuli, involvement of adult healthy human participants over 18 years of age, whose empathy ability showed no difference from the healthy adult, a painless basic state, results presented in Talairach or Montreal Neurological Institute coordinates, multiple studies by the same team as long as they used different raw data. Outcome Measures: Activation likelihood estimation was used to calculate the combined main activated brain regions under the stimulation of pained facial expression. Results: Eight studies were included, containing 178 subjects. Meta-analysis results suggested that the anterior cingulate cortex (BA32), anterior central gyrus (BA44), fusiform gyrus, and insula (BA13) were activated positively as major brain areas under the stimulation of pained facial expression. Conclusion: Our study shows that pained facial expression alone, without viewing of painful stimuli, activated brain regions related to pain empathy, further contributing to revealing the brain's mechanisms of pain empathy.

Background：Several studies using functional magnetic resonance imaging （fMRI） and positron emission tomography （PET） have indicated that cognitive remediation therapy （CRT） might improve cognitive function by changing brain activations in patients with schizophrenia.However,the results were not consistent in these changed brain areas in different studies.The present activation likelihood estimation （ALE） meta-analysis was conducted to investigate whether cognitive function change was accompanied by the brain activation changes,and where the main areas most related to these changes were in schizophrenia patients after CRT.Analyses of whole-brain studies and whole-brain ＋ region of interest （ROI） studies were compared to explore the effect of the different methodologies on the results.Methods：A computerized systematic search was conducted to collect fMRI and PET studies on brain activation changes in schizophrenia patients from pre-to post-CRT.Nine studies using fMRI techniques were included in the meta-analysis.Ginger ALE 2.3.1 was used to perform meta-analysis across these imaging studies.Results：The main areas with increased brain activation were in frontal and parietal lobe,including left medial frontal gyrus,left inferior frontal gyrus,right middle frontal gyrus,right postcentral gyrus,and inferior parietal lobule in patients after CRT,yet no decreased brain activation was found.Although similar increased activation brain areas were identified in ALE with or without ROI studies,analysis including ROI studies had a higher ALE value.Conclusions：The current findings suggest that CRT might improve the cognition of schizophrenia patients by increasing activations of the frontal and parietal lobe.In addition,it might provide more evidence to confirm results by including ROI studies in ALE meta-analysis.

Coordinate-based meta-analyses (CBMA) are very useful for summarizing the large number of voxel-based neuroimaging studies of normal brain functions and brain abnormalities in neuropsychiatric disorders. However, current CBMA methods do not conduct common voxelwise tests, but rather a test of convergence, which relies on some spatial assumptions that data may seldom meet, and has lower statistical power when there are multiple effects. Here we present a new algorithm that can use standard voxelwise tests and, importantly, conducts a standard permutation of subject images (PSI). Its main steps are: a) multiple imputation of study images; b) imputation of subject images; and c) subject-based permutation test to control the familywise error rate (FWER). The PSI algorithm is general and we believe that developers might implement it for several CBMA methods. We present here an implementation of PSI for seed-based d mapping (SDM) method, which additionally benefits from the use of effect sizes, random-effects models, Freedman-Lane-based permutations and threshold-free cluster enhancement (TFCE) statistics, among others. Finally, we also provide an empirical validation of the control of the FWER in SDM-PSI, which showed that it might be too conservative. We hope that the neuroimaging meta-analytic community will welcome this new algorithm and method. •We present a new algorithm for coordinate-based meta-analyses (CBMA) methods.•Opposed to current methods, it conducts common permutation tests.•It may be implemented in several CBMA methods.•We detail and validate its implementation for seed-based d mapping (SDM).

BackgroundEmpathy for pain is a complex phenomenon incorporating sensory, cognitive and affective processes. Functional neuroimaging studies indicate a rich network of brain activations for empathic processing. However, previous research focused on core activations in bilateral anterior insula (AI) and anterior cingulate/anterior midcingulate cortex (ACC/aMCC) which are also typically present during nociceptive (pain) processing. Theoretical understanding of empathy would benefit from empirical investigation of shared and contrasting brain activations for empathic and nociceptive processing.MethodThirty-nine empathy for observed pain studies (1112 participants; 527 foci) were selected by systematic review. Coordinate based meta-analysis (activation likelihood estimation) was performed and novel contrast analyses compared neurobiological processing of empathy with a comprehensive meta-analysis of 180 studies of nociceptive processing (Tanasescu et al., 2016).ResultsConjunction analysis indicated overlapping activations for empathy and nociception in AI, aMCC, somatosensory and inferior frontal regions. Contrast analysis revealed increased likelihood of activation for empathy, relative to nociception, in bilateral supramarginal, inferior frontal and occipitotemporal regions. Nociception preferentially activated bilateral posterior insula, somatosensory cortex and aMCC.ConclusionOur findings support the likelihood of shared and distinct neural networks for empathic, relative to nociceptive, processing. This offers succinct empirical support for recent tiered or modular theoretical accounts of empathy.

Working memory subsumes the capability to memorize, retrieve and utilize information for a limited period of time which is essential to many human behaviours. Moreover, impairments of working memory functions may be found in nearly all neurological and psychiatric diseases. To examine what brain regions are commonly and differently active during various working memory tasks, we performed a coordinate-based meta-analysis over 189 fMRI experiments on healthy subjects. The main effect yielded a widespread bilateral fronto-parietal network. Further meta-analyses revealed that several regions were sensitive to specific task components, e.g. Broca's region was selectively active during verbal tasks or ventral and dorsal premotor cortex were preferentially involved in memory for object identity and location, respectively. Moreover, the lateral prefrontal cortex showed a division in a rostral and a caudal part based on differential involvement in task set and load effects. Nevertheless, a consistent but more restricted “core” network emerged from conjunctions across analyses of specific task designs and contrasts. This “core” network appears to comprise the quintessence of regions, which are necessary during working memory tasks. It may be argued that the core regions form a distributed executive network with potentially generalized functions for focussing on competing representations in the brain. The present study demonstrates that meta-analyses are a powerful tool to integrate the data of functional imaging studies on a (broader) psychological construct, probing the consistency across various paradigms as well as the differential effects of different experimental implementations.

In recent years, there has been substantial growth in neuroimaging studies investigating neural correlates of symbolic (e.g. Arabic numerals) and non-symbolic (e.g. dot arrays) number processing. At present it remains contested whether number is represented abstractly, or if number representations in the brain are format-dependent. In order to quantitatively evaluate the available neuroimaging evidence, we used activation likelihood estimation (ALE) to conduct quantitative meta-analyses of the results reported in 57 neuroimaging papers. Consistent with the existence of an abstract representation of number in the brain, conjunction analyses revealed overlapping activation for symbolic and nonsymbolic numbers in frontal and parietal lobes. Consistent with the notion of format-dependent activation, contrast analyses demonstrated anatomically distinct fronto-parietal activation for symbolic and non-symbolic processing. Therefore, symbolic and non-symbolic numbers are subserved by format-dependent and abstract neural systems. Moreover, the present results suggest that regions across the parietal cortex, not just the intraparietal sulcus, are engaged in both symbolic and non-symbolic number processing, challenging the notion that the intraparietal sulcus is the key region for number processing. Additionally, our analyses indicate that regions in the frontal cortex subserve magnitude representations rather than non-numerical cognitive processes associated with number tasks, thereby highlighting the importance of considering both frontal and parietal regions as important for number processing.

While the discussion on the foundations of social understanding mainly revolves around the notions of empathy, affective mentalizing, and cognitive mentalizing, their degree of overlap versus specificity is still unclear. We took a meta‐analytic approach to unveil the neural bases of cognitive mentalizing, affective mentalizing, and empathy, both in healthy individuals and pathological conditions characterized by social deficits such as schizophrenia and autism. We observed partially overlapping networks for cognitive and affective mentalizing in the medial prefrontal, posterior cingulate, and lateral temporal cortex, while empathy mainly engaged fronto‐insular, somatosensory, and anterior cingulate cortex. Adjacent process‐specific regions in the posterior lateral temporal, ventrolateral, and dorsomedial prefrontal cortex might underpin a transition from representations of cognitive mental states detached from sensory facets to emotionally‐charged representations of affective mental states. Altered mentalizing‐related activity involved distinct sectors of the posterior lateral temporal cortex in schizophrenia and autism, while only the latter group displayed abnormal empathy related activity in the amygdala. These data might inform the design of rehabilitative treatments for social cognitive deficits. The relationship between affective Theory of Mind (ToM), cognitive ToM, and empathy is still unclear. We addressed this issue via coordinate‐based meta‐analyses of previous fMRI data. Empathy and mentalizing engaged frontoinsular and classical ToM nodes, respectively. Adjacent regions might underpin graded transitions between ToM and mentalizing. Autistic and schizophrenic patients displayed specific patterns of altered activity.

Action is a cover term used to refer to a large set of motor processes differing in domain specificities (e.g. execution or observation). Here we review neuroimaging evidence on action processing (N = 416; Subjects = 5912) using quantitative Activation Likelihood Estimation (ALE) and Meta-Analytic Connectivity Modeling (MACM) approaches to delineate the functional specificities of six domains: (1) Action Execution, (2) Action Imitation, (3) Motor Imagery, (4) Action Observation, (5) Motor Learning, (6) Motor Preparation. Our results show distinct functional patterns for the different domains with convergence in posterior BA44 (pBA44) for execution, imitation and imagery processing. The functional connectivity network seeding in the motor-based localized cluster of pBA44 differs from the connectivity network seeding in the (language-related) anterior BA44. The two networks implement distinct cognitive functions. We propose that the motor-related network encompassing pBA44 is recruited when processing movements requiring a mental representation of the action itself.

Neuroimaging studies have improved our understanding of which brain structures are involved in motor learning. Despite this, questions remain regarding the areas that contribute consistently across paradigms with different task demands. For instance, sensorimotor tasks focus on learning novel movement kinematics and dynamics, while serial response time task (SRTT) variants focus on sequence learning. These differing task demands are likely to elicit quantifiably different patterns of neural activity on top of a potentially consistent core network. The current study identified consistent activations across 70 motor learning experiments using activation likelihood estimation (ALE) meta-analysis. A global analysis of all tasks revealed a bilateral cortical–subcortical network consistently underlying motor learning across tasks. Converging activations were revealed in the dorsal premotor cortex, supplementary motor cortex, primary motor cortex, primary somatosensory cortex, superior parietal lobule, thalamus, putamen and cerebellum. These activations were broadly consistent across task specific analyses that separated sensorimotor tasks and SRTT variants. Contrast analysis indicated that activity in the basal ganglia and cerebellum was significantly stronger for sensorimotor tasks, while activity in cortical structures and the thalamus was significantly stronger for SRTT variants. Additional conjunction analyses then indicated that the left dorsal premotor cortex was activated across all analyses considered, even when controlling for potential motor confounds. The highly consistent activation of the left dorsal premotor cortex suggests it is a critical node in the motor learning network. ► Activation likelihood estimation was used to analyze 70 motor learning experiments. ► Analysis revealed a cortico-subcortical network for motor learning. ► Consistent activations were found across subgroups with differing task demands. ► Left dorsal premotor cortex was identified as a key structure in motor learning.