The relation between statistical power and inference in fMRI

Activation Likelihood Estimation Neuroimaging Meta-Analysis: a Powerful Tool for Emotion Research

Over the past two decades, functional magnetic resonance imaging (fMRI) has become the primary tool for exploring neural correlates of emotion. To enhance the reliability of results in understanding the complex nature of emotional experiences, researchers combine findings from multiple fMRI studies using coordinate-based meta-analysis (CBMA). As one of the most widely employed CBMA methods worldwide, activation likelihood estimation (ALE) is of great importance in affective neuroscience and neuropsychology. This comprehensive review provides an introductory guide for implementing the ALE method in emotion research, outlining the experimental steps involved. By presenting a case study about the emotion of disgust, with regard to both its core and social processing, we offer insightful commentary as to how ALE can enable researchers to produce consistent results and, consequently, fruitfully investigate the neural mechanisms underpinning emotions, facilitating further progress in this field.

Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain

Many functional magnetic resonance imaging (fMRI) studies and presurgical mapping applications rely on mass-univariate inference with subsequent multiple comparison correction. Statistical results are frequently visualized as thresholded statistical maps. This approach has inherent limitations including the risk of drawing overly-selective conclusions based only on selective results passing such thresholds. This article gives an overview of both established and newly emerging scientific approaches to supplement such conventional analyses by incorporating information about subthreshold effects with the aim to improve interpretation of findings or leverage a wider array of information. Topics covered include neuroimaging data visualization, p-value histogram analysis and the related Higher Criticism approach for detecting rare and weak effects. Further examples from multivariate analyses and dedicated Bayesian approaches are provided.

Impact of APOE-ε alleles on brain structure and cognitive function in healthy older adults: A VBM and DTI replication study

BACKGROUND

The Apolipoprotein E (APOE) gene has been established in the Alzheimer's disease (AD) literature to impact brain structure and function and may also show congruent effects in healthy older adults, although findings in this population are much less consistent. The current study aimed to replicate and expand the multimodal approach employed by Honea et al. Structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and neuropsychological measures were used to investigate the impact of APOE-ε status on grey matter structure, white matter integrity, and cognitive functioning.

METHODS

Data were obtained from the Alzheimer's Disease Initiative Phase 3 (ADNI3) database. Baseline MRI, DTI and cognitive composite scores for memory (ADNI-Mem) and executive function (ADNI-EF) were acquired from 116 healthy controls. Participants were grouped according to APOE allele presence (APOE-ε2+ N = 17, APOE-ε3ε3 N = 64, APOE-ε4+ N = 35). Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to compare grey matter volume (GMV) and white matter integrity, respectively, between APOE-ε2+ and APOE-ε3ε3 controls, and again between APOE-ε4+ and APOE-ε3ε3 controls. Multivariate analysis of covariance (MANCOVA) was used to examine the effects of APOE polymorphism on memory and EF across all APOE groups with age, sex and education as regressors of no interest. Cognitive scores were correlated (Pearson r) with imaging metrics within groups.

RESULTS

No significant differences were seen across groups, within groups in MRI metrics, or cognitive performance (p>0.05, corrected for multiple comparisons).

CONCLUSIONS

The current study partially replicated and extended previous findings from an earlier multimodal study (Honea 2009). Future studies should clarify APOE mechanisms in healthy ageing by adding other imaging, cognitive, and lifestyle metrics and longitudinal design in larger sample sizes.

Brain Alteration Patterns in Children with Duchenne Muscular Dystrophy: A Machine Learning Approach to Magnetic Resonance Imaging

Background

Duchenne Muscular Dystrophy (DMD) is a genetic disease in which lack of the dystrophin protein causes progressive muscular weakness, cardiomyopathy and respiratory insufficiency. DMD is often associated with other cognitive and behavioral impairments, however the correlation of abnormal dystrophin expression in the central nervous system with brain structure and functioning remains still unclear.

Objective

To investigate brain involvement in patients with DMD through a multimodal and multivariate approach accounting for potential comorbidities.

Methods

We acquired T1-weighted and Diffusion Tensor Imaging data from 18 patients with DMD and 18 age- and sex-matched controls with similar cognitive and behavioral profiles. Cortical thickness, structure volume, fractional anisotropy and mean diffusivity measures were used in a multivariate analysis performed using a Support Vector Machine classifier accounting for potential comorbidities in patients and controls.

Results

the classification experiment significantly discriminates between the two populations (97.2% accuracy) and the forward model weights showed that DMD mostly affects the microstructural integrity of long fiber bundles, in particular in the cerebellar peduncles (bilaterally), in the posterior thalamic radiation (bilaterally), in the fornix and in the medial lemniscus (bilaterally). We also reported a reduced cortical thickness, mainly in the motor cortex, cingulate cortex, hippocampal area and insula.

Conclusions

Our study identified a small pattern of alterations in the CNS likely associated with the DMD diagnosis.

Early life adversity in primates: Behavioral, endocrine, and neural effects

BACKGROUND

Evidence suggests that early life adversity is associated with maladaptive behaviors and is commonly an antecedent of stress-related psychopathology. This is particularly relevant to rearing in primate species as infant primates depend on prolonged, nurturant rearing by caregivers for normal development. To further understand the consequences of early life rearing adversity, and the relation among alterations in behavior, physiology and brain function, we assessed young monkeys that had experienced maternal separation followed by peer rearing with behavioral, endocrine and multimodal neuroimaging measures.

METHODS

50 young rhesus monkeys were studied, half of which were rejected by their mothers and peer reared, and the other half were reared by their mothers. Assessments were performed at approximately 1.8 years of age and included: threat related behavioral and cortisol responses, cerebrospinal fluid (CSF) measurements of oxytocin and corticotropin releasing hormone (CRH), and multimodal neuroimaging measures (anatomical scans, resting functional connectivity, diffusion tensor imaging, and threat-related regional glucose metabolism).

RESULTS

The results demonstrated alterations across behavioral, endocrine, and neuroimaging measures in young monkeys that were reared without their mothers. At a behavioral level in response to a potential threat, peer reared animals engaged in significantly less freezing behavior (p = 0.022) along with increased self-directed behaviors (p < 0.012). Levels of oxytocin in the CSF, but not plasma, were significantly reduced in the peer reared animals (p = 0.019). No differences in plasma cortisol or CSF CRH were observed. Diffusion tensor imaging revealed significantly decreased white matter density across the brain. Exploratory correlational and permutation analyses suggest that the impact of peer rearing on behavior, endocrine and brain structural alterations are mediated by separate parallel mechanisms.

CONCLUSIONS

Taken together, these results demonstrate in NHPs the importance of maternal rearing on the development of brain, behavior and hormonal systems that are linked to social functioning and adaptive responses. The findings suggest that the effects of maternal deprivation are mediated via multiple independent pathways which may account for the heterogeneity in behavioral and biological alterations observed in individuals that have experienced this early life adversity.

Grey matter structural alterations in anxiety disorders: a voxel-based meta-analysis

Anxiety disorders (ADs) are a group of prevalent and destructive mental illnesses, but the current understanding of their underlying neuropathology is still unclear. Employing voxel-based morphometry (VBM), previous studies have demonstrated several common brain regions showing grey matter volume (GMV) abnormalities. However, contradictory results have been reported among these studies. Considering that different subtypes of ADs exhibit common core symptoms despite different diagnostic criteria, and previous meta-analyses have found common core GMV-altered brain regions in ADs, the present research aimed to combine the results of individual studies to identify common GMV abnormalities in ADs. Therefore, we first performed a systematic search in PubMed, Embase, and Web of Science on studies investigating GMV differences between patients with ADs and healthy controls (HCs). Then, the anisotropic effect-size signed differential mapping (AES-SDM) was applied in this meta-analysis. A total of 24 studies (including 25 data sets) were included in the current study, and 906 patients with ADs and 1003 HCs were included. Compared with the HCs, the patients with ADs showed increased GMV in the left superior parietal gyrus, right angular gyrus, left precentral gyrus, and right lingual gyrus, and decreased GMV in the bilateral insula, bilateral thalamus, left caudate, and right putamen. In conclusion, the current study has identified some abnormal GMV brain regions that are related to the pathological mechanisms of anxiety disorders. These findings could contribute to a better understanding of the underlying neuropathology of ADs.

The Emerging Science of Interacting Minds

For over a century, psychology has focused on uncovering mental processes of a single individual. However, humans rarely navigate the world in isolation. The most important determinants of successful development, mental health, and our individual traits and preferences arise from interacting with other individuals. Social interaction underpins who we are, how we think, and how we behave. Here we discuss the key methodological challenges that have limited progress in establishing a robust science of how minds interact and the new tools that are beginning to overcome these challenges. A deep understanding of the human mind requires studying the context within which it originates and exists: social interaction.

Brain-phenotype predictions can survive across diverse real-world data

Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.

Structural and functional brain imaging after treatment with selective-serotonin reuptake-inhibitors in obsessive-compulsive disorder: A mini review

BACKGROUND

Obsessive-compulsive disorder (OCD) is a psychiatric disorder whose etiopathogenesis, according to various neuroimaging studies, seems to be linked to selective dysfunctions in regions within the cortico-striatal-thalamo-cortical circuit. Selective Serotonin Reuptake Inhibitors (SSRIs) are the first-line therapy for OCD but their neurobiological effects on the brain is only partially understood. Therefore, the aim of this review is to highlight structural and functional brain imaging modifications induced by SSRIs treatment.

METHODS

A literature search on PubMed, Psych-Info and Embase database was performed. Studies including patients with OCD that analyzed the effect of SSRIs through structural and functional Magnetic Resonance Imaging were selected. Seven relevant studies were considered eligible for the present review.

RESULTS

Overall, the results of the reviewed studies showed that SSRIs treatment seems to normalize structural, in terms of the white matter and gray matter volumes, and functional activity alterations observed in OCD patients, especially in regions within the prefrontal cortex and striatum.

LIMITATIONS

The poor design of the studies, the small and heterogeneous samples, differences in age, gender, illness course, comorbidities, treatment protocols and the different magnetic fields used make it difficult to generalize the results.

CONCLUSIONS

From the available evidence it emerged that SSRIs treatment has proven to be effective in normalizing brain structural and functional alterations observed in OCD patients. However, future neuroimaging investigations should focus on long-term effects of drugs on brain structure and function in OCD patients through longitudinal approaches in order to identify more effective treatments for these patients.

Mapping expectancy-based appetitive placebo effects onto the brain in women

Suggestions about hunger can generate placebo effects on hunger experiences. But, the underlying neurocognitive mechanisms are unknown. Here, we show in 255 women that hunger expectancies, induced by suggestion-based placebo interventions, determine hunger sensations and economic food choices. Functional magnetic resonance imaging in a subgroup (n = 57/255) provides evidence that the strength of expecting the placebo to decrease hunger moderates medial prefrontal cortex activation at the time of food choice and attenuates ventromedial prefrontal cortex (vmPFC) responses to food value. Dorsolateral prefrontal cortex activation linked to interference resolution formally mediates the suggestion-based placebo effects on hunger. A drift-diffusion model characterizes this effect by showing that the hunger suggestions bias participants' food choices and how much they weigh tastiness against the healthiness of food, which further moderates vmPFC-dlPFC psychophysiological interactions when participants expect decreased hunger. Thus, suggestion-induced beliefs about hunger shape hunger addressing economic choices through cognitive regulation of value computation within the prefrontal cortex.

A research agenda for understanding how social inequality is linked to brain structure and function

Consistent evidence documents powerful effects of social inequality on health, well-being and academic achievement. Yet research on whether social inequality may also be linked to brain structure and function has, until recently, been rare. Here we describe three methodological approaches that can be used to study this question-single site, single study; multi-site, single study; and spatial meta-analysis. We review empirical work that, using these approaches, has observed associations between neural outcomes and structural measures of social inequality-including structural stigma, community-level prejudice, gender inequality, neighbourhood disadvantage and the generosity of the social safety net for low-income families. We evaluate the relative strengths and limitations of these approaches, discuss ethical considerations and outline directions for future research. In doing so, we advocate for a paradigm shift in cognitive neuroscience that explicitly incorporates upstream structural and contextual factors, which we argue holds promise for uncovering the neural correlates of social inequality.

Trying to name what doesn't change: Neural nonresponse to Cognitive Therapy for depression

BACKGROUND

Theoretical models of neural mechanisms underlying Cognitive Behavior Therapy (CBT) for major depressive disorder (MDD) propose that psychotherapy changes neural functioning of prefrontal cortical structures associated with cognitive-control processes (DeRubeis, Siegle, & Hollon, ); however, MDD is persistent and characterized by long-lasting vulnerabilities to recurrence after intervention, suggesting that underlying neural mechanisms of MDD remain despite treatment. It follows that identification of treatment-resistant aberrant neural processes in MDD may inform clinical and research efforts targeting sustained remission. Thus, we sought to identify brain regions showing aberrant neural functioning in MDD that either (1) fail to exhibit substantive change (nonresponse) or (2) exhibit functional changes (response) following CBT.

METHODS

To identify treatment-resistant neural processes (as well as neural processes exhibiting change after treatment), we collected functional magnetic resonance imaging (fMRI) data of MDD patients (n = 58) before and after CBT as well as never-depressed controls (n = 35) before and after a similar amount of time. We evaluated fMRI data using conjunction analyses, which utilized several contrast-based criteria to characterize brain regions showing both differences between patients and controls at baseline and nonresponse or response to CBT.

RESULTS

Findings revealed nonresponse in a cerebellar region and response in prefrontal and parietal regions.

CONCLUSIONS

Results are consistent with prior theoretical models of CBT's direct effect on cortical regulatory processes but expand on them with identification of additional regions (and associated neural systems) of response and nonresponse to CBT.

A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI

Introduction

Computational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of psychiatric neuroimaging. In particular, reinforcement learning (RL) techniques have been widely adopted to examine neural responses to reward prediction errors and stimulus or action values, and how these might vary as a function of clinical status. However, there is a lack of consensus around the importance of the precision of free parameter estimation for these methods, particularly with regard to the learning rate. In the present study, I introduce a novel technique which may be used within a general linear model (GLM) to model the effect of mis-estimation of the learning rate on reward prediction error (RPE)-related neural responses.

Methods

Simulations employed a simple RL algorithm, which was used to generate hypothetical neural activations that would be expected to be observed in functional magnetic resonance imaging (fMRI) studies of RL. Similar RL models were incorporated within a GLM-based analysis method including derivatives, with individual differences in the resulting GLM-derived beta parameters being evaluated with respect to the free parameters of the RL model or being submitted to other validation analyses.

Results

Initial simulations demonstrated that the conventional approach to fitting RL models to RPE responses is more likely to reflect individual differences in a reinforcement efficacy construct (lambda) rather than learning rate (alpha). The proposed method, adding a derivative regressor to the GLM, provides a second regressor which reflects the learning rate. Validation analyses were performed including examining another comparable method which yielded highly similar results, and a demonstration of sensitivity of the method in presence of fMRI-like noise.

Conclusion

Overall, the findings underscore the importance of the lambda parameter for interpreting individual differences in RPE-coupled neural activity, and validate a novel neural metric of the modulation of such activity by individual differences in the learning rate. The method is expected to find application in understanding aberrant reinforcement learning across different psychiatric patient groups including major depression and substance use disorder.

Functional connectivity of cognition-related brain networks in adults with fetal alcohol syndrome

BACKGROUND

Fetal alcohol syndrome (FAS) can result in cognitive dysfunction. Cognitive functions affected are subserved by few functional brain networks. Functional connectivity (FC) in these networks can be assessed with resting-state functional MRI (rs-fMRI). Alterations of FC have been reported in children and adolescents prenatally exposed to alcohol. Previous reports varied substantially regarding the exact nature of findings. The purpose of this study was to assess FC of cognition-related networks in young adults with FAS.

METHODS

Cross-sectional rs-fMRI study in participants with FAS (n = 39, age: 20.9 ± 3.4 years) and healthy participants without prenatal alcohol exposure (n = 44, age: 22.2 ± 3.4 years). FC was calculated as correlation between cortical regions in ten cognition-related sub-networks. Subsequent modelling of overall FC was based on linear models comparing FC between FAS and controls. Results were subjected to a hierarchical statistical testing approach, first determining whether there is any alteration of FC in FAS in the full cognitive connectome, subsequently resolving these findings to the level of either FC within each network or between networks based on the Higher Criticism (HC) approach for detecting rare and weak effects in high-dimensional data. Finally, group differences in single connections were assessed using conventional multiple-comparison correction. In an additional exploratory analysis, dynamic FC states were assessed.

RESULTS

Comparing FAS participants with controls, we observed altered FC of cognition-related brain regions globally, within 7 out of 10 networks, and between networks employing the HC statistic. This was most obvious in attention-related network components. Findings also spanned across subcomponents of the fronto-parietal control and default mode networks. None of the single FC alterations within these networks yielded statistical significance in the conventional high-resolution analysis. The exploratory time-resolved FC analysis did not show significant group differences of dynamic FC states.

CONCLUSIONS

FC in cognition-related networks was altered in adults with FAS. Effects were widely distributed across networks, potentially reflecting the diversity of cognitive deficits in FAS. However, no altered single connections could be determined in the most detailed analysis level. Findings were pronounced in networks in line with attentional deficits previously reported.

A central serotonin regulating gene polymorphism (TPH2) determines vulnerability to acute tryptophan depletion-induced anxiety and ventromedial prefrontal threat reactivity in healthy young men

Serotonin (5-HT) has long been implicated in adaptive emotion regulation as well as the development and treatment of emotional dysregulations in mental disorders. Accumulating evidence suggests a genetic vulnerability may render some individuals at a greater risk for the detrimental effects of transient variations in 5-HT signaling. The present study aimed to investigate whether individual variations in the Tryptophan hydroxylase 2 (TPH2) genetics influence susceptibility for behavioral and neural threat reactivity dysregulations during transiently decreased 5-HT signaling. To this end, interactive effects between TPH2 (rs4570625) genotype and acute tryptophan depletion (ATD) on threat reactivity were examined in a within-subject placebo-controlled pharmacological fMRI trial (n = 51). A priori genotype stratification of extreme groups (GG vs. TT) allowed balanced sampling. While no main effects of ATD on neural reactivity to threat-related stimuli and mood state were observed in the entire sample, accounting for TPH2 genotype revealed an ATD-induced increase in subjective anxious arousal in the GG but not the TT carriers. The effects were mirrored on the neural level, such that ATD specifically reduced ventromedial prefrontal cortex reactivity towards threat-related stimuli in the GG carriers. Furthermore, the ATD-induced increase in subjective anxiety positively associated with the extent of ATD-induced changes in ventromedial prefrontal cortex activity in response to threat-related stimuli in GG carriers. Together the present findings suggest for the first time that individual variations in TPH2 genetics render individuals susceptible to the anxiogenic and neural effects of a transient decrease in 5-HT signaling.

Spatial distribution of hand-grasp motor task activity in spinal cord functional magnetic resonance imaging

Upper extremity motor paradigms during spinal cord functional magnetic resonance imaging (fMRI) can provide insight into the functional organization of the cord. Hand-grasping is an important daily function with clinical significance, but previous studies of similar squeezing movements have not reported consistent areas of activity and are limited by sample size and simplistic analysis methods. Here, we study spinal cord fMRI activation using a unimanual isometric hand-grasping task that is calibrated to participant maximum voluntary contraction (MVC). Two task modeling methods were considered: (1) a task regressor derived from an idealized block design (Ideal) and (2) a task regressor based on the recorded force trace normalized to individual MVC (%MVC). Across these two methods, group motor activity was highly lateralized to the hemicord ipsilateral to the side of the task. Activation spanned C5-C8 and was primarily localized to the C7 spinal cord segment. Specific differences in spatial distribution are also observed, such as an increase in C8 and dorsal cord activity when using the %MVC regressor. Furthermore, we explored the impact of data quantity and spatial smoothing on sensitivity to hand-grasp motor task activation. This analysis shows a large increase in number of active voxels associated with the number of fMRI runs, sample size, and spatial smoothing, demonstrating the impact of experimental design choices on motor activation.

Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection

Resting-state functional magnetic resonance imaging (rs-fMRI) helps characterize regional interactions that occur in the human brain at a resting state. Existing research often attempts to explore fMRI biomarkers that best predict brain disease progression using machine/deep learning techniques. Previous fMRI studies have shown that learning-based methods usually require a large amount of labeled training data, limiting their utility in clinical practice where annotating data is often time-consuming and labor-intensive. To this end, we propose an unsupervised contrastive graph learning (UCGL) framework for fMRI-based brain disease analysis, in which a pretext model is designed to generate informative fMRI representations using unlabeled training data, followed by model fine-tuning to perform downstream disease identification tasks. Specifically, in the pretext model, we first design a bi-level fMRI augmentation strategy to increase the sample size by augmenting blood-oxygen-level-dependent (BOLD) signals, and then employ two parallel graph convolutional networks for fMRI feature extraction in an unsupervised contrastive learning manner. This pretext model can be optimized on large-scale fMRI datasets, without requiring labeled training data. This model is further fine-tuned on to-be-analyzed fMRI data for downstream disease detection in a task-oriented learning manner. We evaluate the proposed method on three rs-fMRI datasets for cross-site and cross-dataset learning tasks. Experimental results suggest that the UCGL outperforms several state-of-the-art approaches in automated diagnosis of three brain diseases (i.e., major depressive disorder, autism spectrum disorder, and Alzheimer's disease) with rs-fMRI data.

The road not taken: Common and distinct neural correlates of regret and relief

Humans anticipate and evaluate both obtained and counterfactual outcomes - outcomes that could have been had an alternate decision been taken - and experience associated emotions of regret and relief. Although many functional magnetic resonance imaging (fMRI) studies have examined the neural correlates of these emotions, there is substantial heterogeneity in their results. We conducted coordinate-based ALE and network-based ANM meta-analysis of fMRI studies of experienced regret and relief to examine commonalities and differences in their neural correlates. Regionally, we observed that the experience of both regret and relief was associated with greater activation in the right ventral striatum (VS), which is implicated in tracking reward prediction error. At the network level, regret and relief shared the reward-sensitive mesocorticolimbic network with preferential activation of the medial orbitofrontal cortex (mOFC) for regret processing and medial cingulate cortex (MCC) for relief processing. Our research identified shared and separable brain systems subserving regret and relief experience, which may inform the treatment of regret-related mood disorders.

Modeling Brain Representations of Words' Concreteness in Context Using GPT-2 and Human Ratings

The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented opportunities to study the human brain in action as language is read and understood. Recent contextualized language models seem to be able to capture homonymic meaning variation ("bat", in a baseball vs. a vampire context), as well as more nuanced differences of meaning-for example, polysemous words such as "book", which can be interpreted in distinct but related senses ("explain a book", information, vs. "open a book", object) whose differences are fine-grained. We study these subtle differences in lexical meaning along the concrete/abstract dimension, as they are triggered by verb-noun semantic composition. We analyze functional magnetic resonance imaging (fMRI) activations elicited by Italian verb phrases containing nouns whose interpretation is affected by the verb to different degrees. By using a contextualized language model and human concreteness ratings, we shed light on where in the brain such fine-grained meaning variation takes place and how it is coded. Our results show that phrase concreteness judgments and the contextualized model can predict BOLD activation associated with semantic composition within the language network. Importantly, representations derived from a complex, nonlinear composition process consistently outperform simpler composition approaches. This is compatible with a holistic view of semantic composition in the brain, where semantic representations are modified by the process of composition itself. When looking at individual brain areas, we find that encoding performance is statistically significant, although with differing patterns of results, suggesting differential involvement, in the posterior superior temporal sulcus, inferior frontal gyrus and anterior temporal lobe, and in motor areas previously associated with processing of concreteness/abstractness.

Neurocognitive Adaptations for Spatial Orientation and Navigation in Astronauts

Astronauts often face orientation challenges while on orbit, which can lead to operator errors in demanding spatial tasks. In this study, we investigated the impact of long-duration spaceflight on the neural processes supporting astronauts' spatial orientation skills. Using functional magnetic resonance imaging (fMRI), we collected data from 16 astronauts six months before and two weeks after their International Space Station (ISS) missions while performing a spatial orientation task that requires generating a mental representation of one's surroundings. During this task, astronauts exhibited a general reduction in neural activity evoked from spatial-processing brain regions after spaceflight. The neural activity evoked in the precuneus was most saliently reduced following spaceflight, along with less powerful effects observed in the angular gyrus and retrosplenial regions of the brain. Importantly, the reduction in precuneus activity we identified was not accounted for by changes in behavioral performance or changes in grey matter concentration. These findings overall show less engagement of explicitly spatial neurological processes at postflight, suggesting astronauts make use of complementary strategies to perform some spatial tasks as an adaptation to spaceflight. These preliminary findings highlight the need for developing countermeasures or procedures that minimize the detrimental effects of spaceflight on spatial cognition, especially in light of planned long-distance future missions.

Conscious expectancy rather than associative strength elicits brain activity during single-cue fear conditioning

The neurocognitive processes underlying Pavlovian conditioning in humans are still largely debated. The conventional view is that conditioned responses (CRs) emerge automatically as a function of the contingencies between a conditioned stimulus (CS) and an unconditioned stimulus (US). As such, the associative strength model asserts that the frequency or amplitude of CRs reflects the strength of the CS-US associations. Alternatively, the expectation model asserts that the presentation of the CS triggers conscious expectancy of the US, which is responsible for the production of CRs. The present study tested the hypothesis that there are dissociable brain networks related to the expectancy and associative strength theories using a single-cue fear conditioning paradigm with a pseudo-random intermittent reinforcement schedule during functional magnetic resonance imaging. Participants' (n = 21) trial-by-trial expectations of receiving shock displayed a significant linear effect consistent with the expectation model. We also found a positive linear relationship between the expectancy model and activity in frontoparietal brain areas including the dorsolateral prefrontal cortex (PFC) and dorsomedial PFC. While an exploratory analysis found a linear relationship consistent with the associated strength model in the insula and early visual cortex, our primary results are consistent with the view that conscious expectancy contributes to CRs.

Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis

Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.

Tree representations of brain structural connectivity via persistent homology

The brain structural connectome is generated by a collection of white matter fiber bundles constructed from diffusion weighted MRI (dMRI), acting as highways for neural activity. There has been abundant interest in studying how the structural connectome varies across individuals in relation to their traits, ranging from age and gender to neuropsychiatric outcomes. After applying tractography to dMRI to get white matter fiber bundles, a key question is how to represent the brain connectome to facilitate statistical analyses relating connectomes to traits. The current standard divides the brain into regions of interest (ROIs), and then relies on an adjacency matrix (AM) representation. Each cell in the AM is a measure of connectivity, e.g., number of fiber curves, between a pair of ROIs. Although the AM representation is intuitive, a disadvantage is the high-dimensionality due to the large number of cells in the matrix. This article proposes a simpler tree representation of the brain connectome, which is motivated by ideas in computational topology and takes topological and biological information on the cortical surface into consideration. We demonstrate that our tree representation preserves useful information and interpretability, while reducing dimensionality to improve statistical and computational efficiency. Applications to data from the Human Connectome Project (HCP) are considered and code is provided for reproducing our analyses.

The effects of laser acupuncture dosage at PC6 (Neiguan) on brain reactivity: a pilot resting-state fMRI study

Previous studies indicated that laser acupuncture (LA) may effectively treat various medical conditions. However, brain responses associated with LA intervention have not been fully investigated. This study is focused on the effect of LA with different energy density (ED) in brain using resting-state functional magnetic resonance imaging (fMRI). We hypothesized that different ED would elicit various brain responses. We enrolled healthy adults participants and selected bilateral PC6 (Neiguan) as the intervention points. LA was applied, respectively, with ED of 0, 7.96, or 23.87 J/cm2. Two 500-s resting-state fMRI scans were acquired before and after intervention, respectively. The functional connectivity (FC) was calculated between autonomic nerve system-regulation associated brainstem structures and other brain regions. Compared to other dosages, the FC between rostral ventrolateral medulla and orbitofrontal cortex has more enhanced; the FC between caudal ventrolateral medulla, nucleus of the solitary tract/nucleus ambiguus, and dorsal motor nucleus of the vagus and somatosensory area has more weakened when ED was 23.87 J/cm2. Different dosages of LA have demonstrated varied regions of FC changes between regions of interest and other brain areas, which indicated that variations in EDs might influence the clinical efficacy and subsequent impacts through distinct neural pathways within the brain.

Quantifying brain connectivity signatures by means of polyconnectomic scoring

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.

Artificial intelligence to empower diagnosis of myelodysplastic syndromes by multiparametric flow cytometry

The diagnosis of myelodysplastic syndromes (MDS) might be challenging and relies on the convergence of cytological, cytogenetic, and molecular factors. Multiparametric flow cytometry (MFC) helps diagnose MDS, especially when other features do not contribute to the decision-making process, but its usefulness remains underestimated, mostly due to a lack of standardization of cytometers. We present here an innovative model integrating artificial intelligence (AI) with MFC to improve the diagnosis and the classification of MDS. We develop a machine learning model through an elasticnet algorithm directed on a cohort of 191 patients, only based on flow cytometry parameters selected by the Boruta algorithm, to build a simple but reliable prediction score with five parameters. Our AI-assisted MDS prediction score greatly improves the sensitivity of the Ogata score while keeping an excellent specificity validated on an external cohort of 89 patients with an Area Under the Curve of 0.935. This model allows the diagnosis of both high- and low-risk MDS with 91.8% sensitivity and 92.5% specificity. Interestingly, it highlights a progressive evolution of the score from clonal hematopoiesis of indeterminate potential (CHIP) to highrisk MDS, suggesting a linear evolution between these different stages. By significantly decreasing the overall misclassification of 52% for patients with MDS and of 31.3% for those without MDS (P=0.02), our AI-assisted prediction score outperforms the Ogata score and positions itself as a reliable tool to help diagnose MDS.

Neuroplastic Changes Associated With Hybrid Exercise-Cognitive Training in Stroke Survivors With Mild Cognitive Decline: A Randomized Controlled Trial

BACKGROUND

Exercise and cognitive training have been shown to induce neuroplastic changes and modulate cognitive function following stroke. However, it remains unclear whether hybridized exercise-cognitive training facilitates cortical activity and further influences cognitive function after stroke.

OBJECTIVE

The study aimed to investigate the effects of 2 hybridized exercise-cognitive trainings on neuroplastic changes and behavioral outcomes in stroke survivors with mild cognitive decline.

METHODS

This study was a single-blind randomized controlled trial. Stroke survivors were randomly assigned to 1 of 3 groups: (1) sequential exercise-cognitive training (SEQ), (2) dual-task exercise-cognitive training (DUAL), or (3) control group (CON). All groups underwent training 60 min per day, 3 days per week, for a total of 12 weeks. The primary outcome was the resting-state (RS) functional connectivity (FC) in functional magnetic resonance imaging. Secondary behavioral outcomes included cognitive and physical functions.

RESULTS

After 12 weeks of training, patients in the SEQ group (n = 21) exhibited increased RS FC between the left occipital lobe and posterior cingulate gyrus with right parietal lobe, compared to the DUAL (n = 22) and CON (n = 20) groups. Additionally, patients in the DUAL group showed increased FC of the left temporal lobe. However, changes in behavioral outcome measures were non-significant among the 3 groups (all P's > .05).

CONCLUSIONS

This study highlights the distinct neuroplastic mechanisms associated with 2 types of exercise-cognitive hybridized trainings. The pre-post functional magnetic resonance imaging measurements illustrated the time course of neural mechanisms for cognitive recovery in stroke survivors following different exercise-cognitive training approaches. Trial registration. NCT03230253.

Imaging the cerebellum in post-traumatic stress and anxiety disorders: a mini-review

Post-traumatic stress disorder (PTSD) and anxiety disorders are among the most prevalent psychiatric conditions worldwide sharing many clinical manifestations and, most likely, neural mechanisms as suggested by neuroimaging research. While the so-called fear circuitry and traditional limbic structures of the brain, particularly the amygdala, have been extensively studied in sufferers of these disorders, the cerebellum has been relatively underexplored. The aim of this paper was to present a mini-review of functional (task-activity or resting-state connectivity) and structural (gray matter volume) results on the cerebellum as reported in magnetic resonance imaging studies of patients with PTSD or anxiety disorders (49 selected studies in 1,494 patients). While mixed results were noted overall, e.g., regarding the direction of effects and anatomical localization, cerebellar structures like the vermis seem to be highly involved. Still, the neurofunctional and structural alterations reported for the cerebellum in excessive anxiety and trauma are complex, and in need of further evaluation.

Conditioning induced placebo-like and nocebo-like effects of thermal discomfort in adults but not in youth

Introduction

Conditioning can be used to modulate the perception of pain, in the form of placebo and nocebo effects. Previous studies show inconsistent results as to whether adolescents show similar, weaker, or non-significant conditioned placebo and nocebo effects compared to effects found in adults. There are suggestions that such differences (if any) may dependent on the cues used in the thermal conditioning paradigms. Therefore, in this current study, we utilized novel, neutral 3D-shaped visual cues to implicitly induce conditioned placebo-like and nocebo-like effects in adolescents and adults.

Methods

During the conditioning paradigm, distinct cues (Fribbles) were paired with low and high temperatures in 24 adults and 20 adolescents (mean age = 25.5 years). In the testing phase, these conditioned cues as well as a neutral (unconditioned) cue were presented with moderate temperatures.

Results

Thermal discomfort of moderate temperatures was lower when presented with the conditioned low heat cue (placebo-like effect) and higher when thermal stimuli were presented with the high heat cue (nocebo-like effect) compared to the neutral cue. The effects were driven by adults, as neither the placebo-like nor the nocebo-like effect was significant in adolescents. The difference between adolescents and adults was not explained by differences in temperature or discomfort levels, as adults and adolescents had comparable calibrated temperatures and levels of discomfort during heat stimuli.

Conclusion

Our findings suggest that thermal perception in adolescents is less influenced by conditioning to an engaging novel visual cue, compared to adults. Our work may have implications for better understanding the scope and limitations of conditioning as a key mechanism of placebo and nocebo effects in youth.

Cingulate prediction of response to antidepressant and cognitive behavioral therapies for depression: Meta-analysis and empirical application

We sought to identify baseline (pre-treatment) neural markers associated with treatment response in major depressive disorder (MDD), specific to treatment type, Cognitive Behavioral Therapy (CBT) or pharmacotherapy (selective serotonin reuptake inhibitors; SSRI). We conducted a meta-analysis of functional magnetic resonance imaging (fMRI) studies to identify neural prognostic indicators of response to CBT or SSRI. To verify the regions derived from literature, the meta-analytic regions were used to predict clinical change in a verification sample of participants with MDD who received either CBT (n = 60) or an SSRI (n = 19) as part of prior clinical trials. The meta-analysis consisted of 21 fMRI studies that used emotion-related tasks. It yielded prognostic regions of the perigenual (meta pgACC) and subgenual anterior cingulate cortex (meta sgACC), associated with SSRI and CBT response, respectively. When applying the meta-analytic regions to predict treatment response in the verification sample, reactivity of the meta pgACC was prognostic for SSRI response, yet the effect direction was opposite of most prior studies. Meta sgACC reactivity failed to be prognostic for CBT response. Results confirm the prognostic potential of neural reactivity of ACC subregions in MDD but further research is necessary for clinical translation.

The challenges and prospects of brain-based prediction of behaviour

Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.

Connectome-based machine learning models are vulnerable to subtle data manipulations

Neuroimaging-based predictive models continue to improve in performance, yet a widely overlooked aspect of these models is "trustworthiness," or robustness to data manipulations. High trustworthiness is imperative for researchers to have confidence in their findings and interpretations. In this work, we used functional connectomes to explore how minor data manipulations influence machine learning predictions. These manipulations included a method to falsely enhance prediction performance and adversarial noise attacks designed to degrade performance. Although these data manipulations drastically changed model performance, the original and manipulated data were extremely similar (r = 0.99) and did not affect other downstream analysis. Essentially, connectome data could be inconspicuously modified to achieve any desired prediction performance. Overall, our enhancement attacks and evaluation of existing adversarial noise attacks in connectome-based models highlight the need for counter-measures that improve the trustworthiness to preserve the integrity of academic research and any potential translational applications.

Brain responses to nutrients are severely impaired and not reversed by weight loss in humans with obesity: a randomized crossover study

Post-ingestive nutrient signals to the brain regulate eating behaviour in rodents, and impaired responses to these signals have been associated with pathological feeding behaviour and obesity. To study this in humans, we performed a single-blinded, randomized, controlled, crossover study in 30 humans with a healthy body weight (females N = 12, males N = 18) and 30 humans with obesity (females N = 18, males N = 12). We assessed the effect of intragastric glucose, lipid and water (noncaloric isovolumetric control) infusions on the primary endpoints cerebral neuronal activity and striatal dopamine release, as well as on the secondary endpoints plasma hormones and glucose, hunger scores and caloric intake. To study whether impaired responses in participants with obesity would be partially reversible with diet-induced weight loss, imaging was repeated after 10% diet-induced weight loss. We show that intragastric glucose and lipid infusions induce orosensory-independent and preference-independent, nutrient-specific cerebral neuronal activity and striatal dopamine release in lean participants. In contrast, participants with obesity have severely impaired brain responses to post-ingestive nutrients. Importantly, the impaired neuronal responses are not restored after diet-induced weight loss. Impaired neuronal responses to nutritional signals may contribute to overeating and obesity, and ongoing resistance to post-ingestive nutrient signals after significant weight loss may in part explain the high rate of weight regain after successful weight loss.

Network targets for therapeutic brain stimulation: towards personalized therapy for pain

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.

Oxytocin normalizes the implicit processing of fearful faces in psychopathy: a randomized crossover study using fMRI

Adults with antisocial personality disorder with (ASPD + P) and without (ASPD - P) psychopathy commit the majority of violent crimes. Empathic processing abnormalities are particularly prominent in psychopathy, but effective pharmacological interventions have yet to be identified. Oxytocin modulates neural responses to fearful expressions in healthy populations. The current study investigates its effects in violent antisocial men. In a placebo-controlled, randomized crossover design, 34 violent offenders (19 ASPD + P; 15 ASPD - P) and 24 healthy non-offenders received 40 IU intranasal oxytocin or placebo and then completed an fMRI morphed faces task examining the implicit processing of fearful facial expressions. Increasing intensity of fearful facial expressions failed to appropriately modulate activity in the bilateral mid-cingulate cortex in violent offenders with ASPD + P, compared with those with ASPD - P. Oxytocin abolished these group differences. This represents evidence of neurochemical modulation of the empathic processing of others' distress in psychopathy.

Value-based neural representations predict social decision preferences

Social decision-making is omnipresent in everyday life, carrying the potential for both positive and negative consequences for the decision-maker and those closest to them. While evidence suggests that decision-makers use value-based heuristics to guide choice behavior, very little is known about how decision-makers' representations of other agents influence social choice behavior. We used multivariate pattern expression analyses on fMRI data to understand how value-based processes shape neural representations of those affected by one's social decisions and whether value-based encoding is associated with social decision preferences. We found that stronger value-based encoding of a given close other (e.g. parent) relative to a second close other (e.g. friend) was associated with a greater propensity to favor the former during subsequent social decision-making. These results are the first to our knowledge to explicitly show that value-based processes affect decision behavior via representations of close others.

A pharmacological challenge paradigm to assess neural signatures of script-elicited acute dissociation in women with post-traumatic stress disorder

BACKGROUND

There is limited experimentally controlled neuroimaging research available that could explain how dissociative states occur and which neurobiological changes are involved in acute post-traumatic dissociation.

AIMS

To test the causal hypothesis that acute dissociation is triggered bottom-up by a selective noradrenergic-mediated increase in amygdala activation during the processing of autobiographical trauma memories.

METHOD

Women with post-traumatic stress disorder (n = 47) and a history of interpersonal childhood trauma underwent a within-participant, placebo-controlled pharmacological challenge paradigm (4.0 mg reboxetine versus placebo) employing script-driven imagery (traumatic versus neutral autobiographical memory recall). Script-elicited brain activation patterns (measured via functional magnetic resonance imagery) were analysed by means of whole-brain analyses and a pre-registered region of interest (i.e. amygdala).

RESULTS

Self-reported acute dissociation increased significantly during trauma (versus neutral) recall but did not differ between pharmacological conditions. The pharmacological manipulation was also unsuccessful in eliciting increased amygdala activation following script-driven imagery in the reboxetine (versus placebo) condition. In the reboxetine condition, trauma retrieval resulted in similar activation patterns as in the placebo condition (e.g. elevated brain activation in the middle occipital gyrus and supramarginal gyrus), albeit with different peaks.

CONCLUSIONS

Current (null) findings cast doubt on the suggested role of the amygdala in subserving dissociative processing of trauma memories. Alternative pharmacological manipulation approaches (e.g. ketamine) and analysis techniques (e.g. event-related independent component analysis) might provide better insight into the spatiotemporal dynamics and network shifts involved in dissociative experiences and autobiographical trauma memory recall.

Highlight Results, Don't Hide Them: Enhance interpretation, reduce biases and improve reproducibility

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples and extensions of this approach for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams' results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice-focusing on highlighting results, rather than hiding all but the strongest ones-can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.

Re-evaluating frontopolar and temporoparietal contributions to detection and discrimination confidence

Previously, we identified a subset of regions where the relation between decision confidence and univariate functional magnetic resonance imaging (fMRI) activity was quadratic, with stronger activation for both high and low compared with intermediate levels of confidence. We further showed that, in a subset of these regions, this quadratic modulation appeared only for confidence in detection decisions about the presence or absence of a stimulus, and not for confidence in discrimination decisions about stimulus identity (Mazor et al. 2021). Here, in a pre-registered follow-up experiment, we sought to replicate our original findings and identify the origins of putative detection-specific confidence signals by introducing a novel asymmetric-discrimination condition. The new condition required discriminating two alternatives but was engineered such that the distribution of perceptual evidence was asymmetric, just as in yes/no detection. We successfully replicated the quadratic modulation of subjective confidence in prefrontal, parietal and temporal cortices. However, in contrast with our original report, this quadratic effect was similar in detection and discrimination responses, but stronger in the novel asymmetric-discrimination condition. We interpret our findings as weighing against the detection-specificity of confidence signatures and speculate about possible alternative origins of a quadratic modulation of decision confidence.

Statistical power in network neuroscience

Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.

Neuroanatomical correlates of system-justifying ideologies: a pre-registered voxel-based morphometry study on right-wing authoritarianism and social dominance orientation

System-justifying ideologies are a cluster of ideals that perpetuate a hierarchical social system despite being fraught with inequalities. Right-wing authoritarianism (RWA) and social dominance orientation (SDO) are two ideologies that have received much attention in the literature separately and together. Given that these ideologies are considered to be stable individual differences that are likely to have an evolutionary basis, there has yet to be any examination for volumetric brain structures associated with these variables. Here, we proposed an investigation of overlapping and non-overlapping brain regions associated with RWA and SDO in a sample recruited in Singapore. Indeed, it will be interesting to determine how RWA and SDO correlate in a country that proactively promotes institutionalized multi-culturalism such as Singapore. RWA and SDO scores were collected via self-report measures from healthy individuals (39 males and 43 females; age 25.89 ± 5.68 years). Consequently, voxel-based morphometry (VBM) whole brain and region of interest (ROI) analyses were employed to identify neuroanatomical correlates of these system-justifying ideologies. RWA and SDO scores were strongly correlated despite the low ideological contrast in Singapore's sociopolitical context. The whole brain analysis did not reveal any significant clusters associated with either RWA or SDO. The ROI analyses revealed clusters in the bilateral amygdala and ventromedial prefrontal cortex (vmPFC) that were associated with both RWA and SDO scores, whereas two clusters in the left anterior insula were negatively associated with only SDO scores. The study corroborates the claim of RWA and SDO as stable individual differences with identifiable neuroanatomical correlates, but our exploratory analysis suggests evidence that precludes any definitive conclusion based on the present evidence.

The generalization of behavioral control over physical threats to social stressors in humans: A pilot fMRI study

Behavioral control, the ability to manage one's exposure to a given stressor, influences the impacts of both the present and future stressors. Behavioral control over a stressor may decrease stress caused by the stressor, and promote resilience towards future stressors. A lack of behavioral control may exacerbate the stress response and lead to learned helplessness, a generalized view that one cannot control other, unrelated stressors in their environment. The ventromedial prefrontal cortex (vmPFC) may detect the presence of behavioral control over a stressor and communicate this to subcortical regions involved in stress responses, such as the nucleus accumbens (NAc). Building on previous research in animals and humans, we piloted a paradigm to investigate how behavioral control over a physical threat (electric shocks), generalized to responses for a subsequent social stressor (anticipation of public speaking). Our manipulation of behavioral control effected perceived control between groups, increased stress across but not between groups, and no effects generalized to the subsequent social stressor in behavioral, physiological, or neural responses. We discuss refinements to the paradigm to strengthen the manipulation, the potential impacts of statistical power on the present results, and metrics to measure the generalization of behavioral control in addition to vmPFC-subcortical connectivity.

Neuroanatomical correlates of system-justifying ideologies: a pre-registered voxel-based morphometry study on right-wing authoritarianism and social dominance orientation

System-justifying ideologies are a cluster of ideals that perpetuate a hierarchical social system despite being fraught with inequalities. Right-wing authoritarianism (RWA) and social dominance orientation (SDO) are two ideologies that have received much attention in the literature separately and together. Given that these ideologies are considered to be stable individual differences that are likely to have an evolutionary basis, there has yet to be any examination for volumetric brain structures associated with these variables. Here, we proposed an investigation of overlapping and non-overlapping brain regions associated with RWA and SDO in a sample recruited in Singapore. Indeed, it will be interesting to determine how RWA and SDO correlate in a country that proactively promotes institutionalized multi-culturalism such as Singapore. RWA and SDO scores were collected via self-report measures from healthy individuals (39 males and 43 females; age 25.89 ± 5.68 years). Consequently, voxel-based morphometry (VBM) whole brain and region of interest (ROI) analyses were employed to identify neuroanatomical correlates of these system-justifying ideologies. RWA and SDO scores were strongly correlated despite the low ideological contrast in Singapore's sociopolitical context. The whole brain analysis did not reveal any significant clusters associated with either RWA or SDO. The ROI analyses revealed clusters in the bilateral amygdala and ventromedial prefrontal cortex (vmPFC) that were associated with both RWA and SDO scores, whereas two clusters in the left anterior insula were negatively associated with only SDO scores. The study corroborates the claim of RWA and SDO as stable individual differences with identifiable neuroanatomical correlates, but our exploratory analysis suggests evidence that precludes any definitive conclusion based on the present evidence.

Dorsal attention network activity during perceptual organization is distinct in schizophrenia and predictive of cognitive disorganization

Visual shape completion is a canonical perceptual organization process that integrates spatially distributed edge information into unified representations of objects. People with schizophrenia show difficulty in discriminating completed shapes, but the brain networks and functional connections underlying this perceptual difference remain poorly understood. Also unclear is whether brain network differences in schizophrenia occur in related illnesses or vary with illness features transdiagnostically. To address these topics, we scanned (functional magnetic resonance imaging, fMRI) people with schizophrenia, bipolar disorder, or no psychiatric illness during rest and during a task in which they discriminated configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping was used to evaluate the likely involvement of resting-state connections for shape completion. Illusory/fragmented task activation differences ('modulations') in the dorsal attention network (DAN) could distinguish people with schizophrenia from the other groups (AUCs > .85) and could transdiagnostically predict cognitive disorganization severity. Activity flow over functional connections from the DAN could predict secondary visual network modulations in each group, except in schizophrenia. The secondary visual network was strongly and similarly modulated in each group. Task modulations were dispersed over more networks in patients compared to controls. In summary, DAN activity during visual perceptual organization is distinct in schizophrenia, symptomatically relevant, and potentially related to improper attention-related feedback into secondary visual areas.

Emotional descriptions increase accidental harm punishment and its cortico-limbic signatures during moral judgment in autism

Individuals with autism spectrum disorder (ASD) present difficulties in integrating mental state information in complex moral tasks. Yet, ASD research has not examined whether this process is influenced by emotions, let alone while capturing its neural bases. We investigated how language-induced emotions modulate intent-based moral judgment in ASD. In a fMRI task, 30 adults with ASD and 27 neurotypical controls read vignettes whose protagonists commit harm either accidentally or intentionally, and then decided how much punishment the protagonist deserved. Emotional content was manipulated across scenarios through the use of graphic language (designed to trigger arousing negative responses) vs. plain (just-the-facts, emotionless) language. Off-line functional connectivity correlates of task performance were also analyzed. In ASD, emotional (graphic) descriptions amplified punishment ratings of accidental harms, associated with increased activity in fronto-temporo-limbic, precentral, and postcentral/supramarginal regions (critical for emotional and empathic processes), and reduced connectivity among the orbitofrontal cortex and the angular gyrus (involved in mentalizing). Language manipulation did not influence intentional harm processing in ASD. In conclusion, in arousing and ambiguous social situations that lack intentionality clues (i.e. graphic accidental harm scenarios), individuals with ASD would misuse their emotional responses as the main source of information to guide their moral decisions. Conversely, in face of explicit harmful intentions, they would be able to compensate their socioemotional alterations and assign punishment through non-emotional pathways. Despite limitations, such as the small sample size and low ecological validity of the task, results of the present study proved reliable and have relevant theoretical and translational implications.

Correlates of poor insight: A comparative fMRI and sMRI study in obsessive-compulsive disorder and schizo-obsessive disorder

BACKGROUND

Despite the several researches on the correlates of insight in psychosis, less is known regarding the specificity of disease diagnosis on the relationship between insight and the correlates. The current study sought to explore the effects of insight and disease diagnosis on those in patients with obsessive-compulsive disorder (OCD) and patients with schizo-obsessive disorder (SOD).

METHODS

We evaluated clinical symptoms and neurocognitions among 111 patients (including 41 OCD with good insight, 40 OCD with poor insight, 14 SOD with good insight and 16 SOD with poor insight. Gray matter volume and spontaneous neural activity were also examined by analyzing the voxel-based morphometry and amplitude of low frequency fluctuation (ALFF), respectively.

RESULTS

Interactive effects of insight and diagnosis was found on working memory and the gray matter volume in right superior and middle temporal gyrus. Main effect of insight was found on working and visual memory, compulsion and obsession, and ALFF in right middle and superior occipital cortex. Main effect of diagnosis was found on severity of compulsion, relative verbal IQ, executive function, verbal and visual memory, working memory and ALFF in precuneus, medial superior frontal gyrus, anterior cingulate and paracingulate gyri, and inferior parietal, postcentral gyrus, paracentral lobule.

CONCLUSIONS

As a common feature in mental disorders, insight has its own special influence on neurocognition and possible structural/functional alterations in brain, and the influence is partly dependent of disease diagnosis.

Social reappraisal of emotions is linked with the social presence effect in the default mode network

Introduction

Social reappraisal, during which one person deliberately tries to regulate another's emotions, is a powerful cognitive form of social emotion regulation, crucial for both daily life and psychotherapy. The neural underpinnings of social reappraisal include activity in the default mode network (DMN), but it is unclear how social processes influence the DMN and thereby social reappraisal functioning. We tested whether the mere presence of a supportive social regulator had an effect on the DMN during rest, and whether this effect in the DMN was linked with social reappraisal-related neural activations and effectiveness during negative emotions.

Methods

A two-part fMRI experiment was performed, with a psychotherapist as the social regulator, involving two resting state (social, non-social) and two task-related (social reappraisal, social no-reappraisal) conditions.

Results

The psychotherapist's presence enhanced intrinsic functional connectivity of the dorsal anterior cingulate (dACC) within the anterior medial DMN, with the effect positively related to participants' trust in psychotherapists. Secondly, the social presence-induced change in the dACC was related with (a) the social reappraisal-related activation in the bilateral dorsomedial/dorsolateral prefrontal cortex and the right temporoparietal junction and (b) social reappraisal success, with the latter relationship moderated by trust in psychotherapists.

Conclusion

Results demonstrate that a psychotherapist's supportive presence can change anterior medial DMN's intrinsic connectivity even in the absence of stimuli and that this DMN change during rest is linked with social reappraisal functioning during negative emotions. Data suggest that trust-dependent social presence effects on DMN states are relevant for social reappraisal-an idea important for daily-life and psychotherapy-related emotion regulation.

Bilateral theta-burst stimulation on emotional processing in major depressive disorder: A functional neuroimaging study from a randomized, double-blind, sham-controlled trial

AIM

Bilateral theta-burst stimulation (biTBS; intermittent TBS over the left dorsolateral prefrontal cortex [DLPFC] and continuous TBS over the right DLPFC) has demonstrated efficacy in improving symptoms in patients with major depressive disorder (MDD). However, the underlying brain mechanisms remain unknown. The authors aimed to investigate the antidepressant efficacy of biTBS monotherapy and its effects on the brain responses measured by functional magnetic resonance imaging (fMRI) during emotional processing in MDD.

METHODS

The authors conducted a double-blind, randomized, sham-controlled trial of patients with MDD who exhibited no responses to at least one adequate antidepressant treatment for the prevailing episode. Recruited patients were randomly assigned to 10 biTBS monotherapy or sham stimulation sessions. The fMRI scans during performing emotional recognition task were obtained at baseline and after 10 sessions of treatment. Depressive symptoms were assessed using the 21-item Hamilton Rating Scale for Depression at baseline and the weeks 4, 8, 12, 16, 20, and 24 week.

RESULTS

The biTBS group (n = 17) exhibited significant decreases in depression scores compared with the sham group (n = 11) at week 8 (70% vs 40%; P = 0.02), and the significant differences persisted during the 24-week follow-up periods. At week 4, when the treatment course was completed, patients in the biTBS group, but not in the sham group, exhibited increased brain activities over the left superior and middle frontal gyrus during negative emotional stimuli.

CONCLUSION

The authors' findings provide the first evidence regarding the underlying neural mechanisms of biTBS therapy to improve clinical symptoms in patients with MDD.

Neuroanatomical predictors of real‐time fMRI ‐based anterior insula regulation. A supervised machine learning study

Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.