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Social and non-social deficits in autism spectrum disorders (ASD) persist into adulthood and may share common regions of aberrant neural activations. The current meta-analysis investigated activation differences between ASD and neurotypical controls irrespective of task type. Activation likelihood estimation meta-analyses were performed to examine consistent hypo-activated and/or hyper-activated regions for all tasks combined, and for social and non-social tasks separately; meta-analytic connectivity modelling and behavioral/paradigm analyses were performed to examine co-activated regions and associated behaviors. One hundred studies (mean age range = 18–41 years) were included. For all tasks combined, the ASD group showed significant (p 3, maximum ALE = 0.0327), and this cluster co-activated with two other clusters around the right cerebellum (peak 42, -56, -22, volume = 2560mm3, maximum ALE = 0.049) Lobule VI/Crus I and the left fusiform gyrus (BA47) (peak − 42, -46, -18, volume = 1616 mm3, maximum ALE = 0.046) and left cerebellum (peak − 42, -58, -20, volume = 1616mm3, maximum ALE = 0.033) Lobule VI/Crus I. While the left amygdala was associated with negative emotion (fear) (z = 3.047), the left fusiform gyrus/cerebellum Lobule VI/Crus I cluster was associated with language semantics (z = 3.724) and action observation (z = 3.077). These findings highlight the left amygdala as a region consistently hypo-activated in ASD and suggest the potential involvement of fusiform gyrus and cerebellum in social cognition in ASD. Future research should further elucidate if and how amygdala-fusiform/cerebellar connectivity relates to social and non-social cognition in adults with ASD.

This study compared acoustic and neural changes accompanying two treatments matched for intensive dosage but having two different treatment targets (voice or articulation) to dissociate the effects of treatment target and intensive dosage in speech therapies. Nineteen participants with Parkinsonian dysphonia (11 F) were randomized to three groups: intensive treatment targeting voice (voice group, n = 6), targeting articulation (articulation group, n = 7), or an untreated group (no treatment, n = 6). The severity of dysphonia was assessed by the smoothed cepstral peak prominence (CPPS) and neuronal changes were evaluated by cerebral blood flow (CBF) recorded at baseline, posttreatment, and 7-month follow-up. Only the voice treatment resulted in significant posttreatment improvement in CPPS, which was maintained at 7 months. Following voice treatment, increased activity in left premotor and bilateral auditory cortices was observed at posttreatment, and in the left motor and auditory cortices at 7-month follow-up. Articulation treatment resulted in increased activity in bilateral premotor and left insular cortices that were sustained at a 7-month follow-up. Activation in the auditory cortices and a significant correlation between the CPPS and CBF in motor and auditory cortices was observed only in the voice group. The intensive dosage resulted in long-lasting behavioral and neural effects as the no-treatment group showed a progressive decrease in activity in areas of the speech motor network out to a 7-month follow-up. These results indicate that dysphonia and the speech motor network can be differentially modified by treatment targets, while intensive dosage contributes to long-lasting effects of speech treatments.

IntroductionThe cocktail-party problem refers to the difficulty listeners face when trying to attend to relevant sounds that are mixed with irrelevant ones. Previous studies have shown that solving these problems relies on perceptual as well as cognitive processes. Previously, we showed that speech-reception thresholds (SRTs) on a cocktail-party listening task were influenced by genetic factors. Here, we estimated the degree to which these genetic factors overlapped with those influencing cognitive abilities.MethodsWe measured SRTs and hearing thresholds (HTs) in 493 listeners, who ranged in age from 18 to 91 years old. The same individuals completed a cognitive test battery comprising 18 measures of various cognitive domains. Individuals belonged to large extended pedigrees, which allowed us to use variance component models to estimate the narrow-sense heritability of each trait, followed by phenotypic and genetic correlations between pairs of traits.ResultsAll traits were heritable. The phenotypic and genetic correlations between SRTs and HTs were modest, and only the phenotypic correlation was significant. By contrast, all genetic SRT–cognition correlations were strong and significantly different from 0. For some of these genetic correlations, the hypothesis of complete pleiotropy could not be rejected.DiscussionOverall, the results suggest that there was substantial genetic overlap between SRTs and a wide range of cognitive abilities, including abilities without a major auditory or verbal component. The findings highlight the important, yet sometimes overlooked, contribution of higher-order processes to solving the cocktail-party problem, raising an important caveat for future studies aiming to identify specific genetic factors that influence cocktail-party listening.

The literature of neuroimaging meta-analysis has been thriving for over a decade. A majority of them were coordinate-based meta-analyses, particularly the activation likelihood estimation (ALE) approach. A meta-evaluation of these meta-analyses was performed to qualitatively evaluate their design and reporting standards. The publications listed from the BrainMap website were screened. Six hundred and three ALE papers published during 2010–2019 were included and analysed. For reporting standards, most of the ALE papers reported their total number of Papers involved and mentioned the inclusion/exclusion criteria on Paper selection. However, most papers did not describe how data redundancy was avoided when multiple related Experiments were reported within one paper. The most prevalent repeated-measures correction methods were voxel-level FDR (54.4%) and cluster-level FWE (33.8%), with the latter quickly replacing the former since 2016. For study characteristics, sample size in terms of number of Papers included per ALE paper and number of Experiments per analysis seemed to be stable over the decade. One-fifth of the surveyed ALE papers failed to meet the recommendation of having >17 Experiments per analysis. For data sharing, most of them did not provide input and output data. In conclusion, the field has matured well in terms of rising dominance of cluster-level FWE correction, and slightly improved reporting on elimination of data redundancy and providing input data. The provision of Data and Code availability statements and flow chart of literature screening process, as well as data submission to BrainMap, should be more encouraged.

Restriction spectrum imaging-magnetic resonance imaging is a short duration enhanced diffusion-weighted technique that seeks to standardize sequences and predict upgrading. We test this technology for active surveillance biopsies. Our objective is to investigate the utility of restriction spectrum imaging-magnetic resonance imaging to improve upgrading detection in a prostate cancer active surveillance cohort.We prospectively enrolled men on active surveillance undergoing repeat biopsy from January 2016 to June 2019. Subjects underwent prostate multiparametric magnetic resonance imaging and restriction spectrum imaging-magnetic resonance imaging reviewed by a urological radiologist for PI-RADS® scored lesions, followed by magnetic resonance imaging-guided prostate biopsy by a urologist. Restriction spectrum imaging-magnetic resonance imaging analysis with proprietary research software (CorTechs Labs, San Diego, California) generated a restricted signal map. We compared the restricted signal map and apparent diffusion coefficient values using T-test, ANOVA, and logistic regression analyses for prediction of upgrading.Of 123 enrolled men we identified 74 restriction spectrum imaging-magnetic resonance imaging regions of interest (targeted lesions) in 110 subjects, with 105 subjects completing biopsy. The restricted signal map was significant per PI-RADS score for true-positive lesion detection (mean difference 28, SD 0.7, p=0.001), and better than apparent diffusion coefficient (mean difference -15, SD 55, p=0.6). Restriction spectrum imaging generated restricted signal map values50 improved sensitivity, specificity, positive predictive value and negative predictive value (81.0%, 81.8%, 54.2% and 94.2%) over PI-RADS ≥3 (71.4%, 38.9%, 23.7% and 83.7%, respectively) for Gleason upgrading. Overall restriction spectrum imaging is able to improve the AUC of 0.70 (95% CI 0.49-0.92, p=0.03) to 0.90 (95% CI 0.82-0.98, p0.001).Restriction spectrum imaging-magnetic resonance imaging enhances the standard PI-RADS system by providing a noninvasive radiological biomarker to predict upgrading in active surveillance.

Over the past decades, powerful MRI‐based methods have been developed, which yield both voxel‐based maps of the brain activity and anatomical variation related to different conditions. With regard to functional or structural MRI data, forward inferences try to determine which areas are involved given a mental function or a brain disorder. A major drawback of forward inference is its lack of specificity, as it suggests the involvement of brain areas that are not specific for the process/condition under investigation. Therefore, a different approach is needed to determine to what extent a given pattern of cerebral activation or alteration is specifically associated with a mental function or brain pathology. In this study, we present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived‐maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology., We present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived‐maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology.