The relation between statistical power and inference in fMRI

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.

Leveraging edge-centric networks complements existing network-level inference for functional connectomes

The human connectome is modular with distinct brain regions clustering together to form large-scale communities, or networks. This concept has recently been leveraged in novel inferencing procedures by averaging the edge-level statistics within networks to induce more powerful inferencing at the network level. However, these networks are constructed based on the similarity between pairs of nodes. Emerging work has described novel edge-centric networks, which instead use the similarity between pairs of edges to construct networks. In this work, we use these edge-centric networks in a network-level inferencing procedure and compare this novel method to traditional inferential procedures and the network-level procedure using node-centric networks. We use data from the Human Connectome Project, the Healthy Brain Network, and the Philadelphia Neurodevelopmental Cohort and use a resampling technique with various sample sizes (n=40, 80, 120) to probe the power and specificity of each method. Across datasets and sample sizes, using the edge-centric networks outperforms using node-centric networks for inference as well as edge-level FDR correction and NBS. Additionally, the edge-centric networks were found to be more consistent in clustering effect sizes of similar values as compared to node-centric networks, although node-centric networks often had a lower average within-network effect size variability. Together, these findings suggest that using edge-centric networks for network-level inference can procure relatively powerful results while remaining similarly accurate to the underlying edge-level effects across networks, complementing previous inferential methods.

An examination of volumetric and cortical thickness correlates of posttraumatic nightmares in male active duty service members

Posttraumatic nightmares commonly occur after a traumatic experience. Despite significant deleterious effects on well-being and their role in posttraumatic stress disorder, posttraumatic nightmares remain understudied. The neuroanatomical structures of the amygdala, medial prefrontal cortex, hippocampus, and anterior cingulate cortex constitute the AMPHAC model (Levin and Nielsen, 2007), which is implicated in the neurophysiology of disturbing dreams of which posttraumatic nightmares is a part. However, this model has not been investigated using neuroimaging data. The present study sought to determine whether there are structural differences in the AMPHAC regions in relation to the occurrence of posttraumatic nightmares. Data were obtained from treatment-seeking male active duty service members (N = 351). Posttraumatic nightmares were not significantly related to gray matter volume, cortical surface area, or cortical thickness of any the AMPHAC regions when controlling for age and history of mild traumatic brain injury. Although the present analyses do not support an association between structural measures of AMPHAC regions and posttraumatic nightmares, we suggest that functional differences within and/or between these brain regions may be related to the occurrence of posttraumatic nightmares because functional and structural associations are distinct. Future research should examine whether functional differences may be associated with posttraumatic nightmares.

Associations between disrupted functional brain network topology and cognitive impairment in patients with rectal cancer during chemotherapy

Introduction

Cognitive impairment has been identified in patients with non-central nervous system cancer received chemotherapy. Chemotherapy-induced changes in the brain are considered as the possible causes of the cognitive deficits of patients. This study aimed to explore chemotherapy-related functional brain changes and cognitive impairment in rectal cancer (RC) patients who had just finished chemotherapy treatment.

Methods

In this study, RC patients after chemotherapy (on the day patients received the last dose of chemotherapy) (n=30) and matched healthy controls (HCs) (n=30) underwent cognitive assessments, structural magnetic resonance imaging (MRI) and resting-state functional MRI. The functional brain networks were constructed by thresholding the partial correlation matrices of 90 brain regions in the Anatomical Automatic Labeling template and the topologic properties were evaluated by graph theory analysis. Moreover, correlations between altered topological measures and scores of cognitive scales were explored in the patient group.

Results

Compared with HCs, RC patients had lower scores of cognitive scales. The functional brain network had preserved small-world topological features but with a tendency towards higher path length in the whole network. In addition, patients had decreased nodal global efficiency (E_glo(i)) in the left superior frontal gyrus (dorsolateral), superior frontal gyrus (orbital part), inferior frontal gyrus (opercular part), inferior frontal gyrus (triangular part) and right inferior frontal gyrus (triangular part). Moreover, values of E_glo(i) in the superior and inferior frontal gyrus were positively associated with cognitive function in the patient group.

Conclusion

These results suggested that cognitive impairment was associated with disruptions of the topological organization in functional brain networks of RC patients who had just finished chemotherapy, which provided new insights into the pathophysiology underlying acute effects of chemotherapy on cognitive function.

A randomized pharmacological fMRI trial investigating D-cycloserine and brain plasticity mechanisms in learned pain responses

Learning and negative outcome expectations can increase pain sensitivity, a phenomenon known as nocebo hyperalgesia. Here, we examined how a targeted pharmacological manipulation of learning would impact nocebo responses and their brain correlates. Participants received either a placebo (n = 27) or a single 80 mg dose of D-cycloserine (a partial NMDA receptor agonist; n = 23) and underwent fMRI. Behavioral conditioning and negative suggestions were used to induce nocebo responses. Participants underwent pre-conditioning outside the scanner. During scanning, we first delivered baseline pain stimulations, followed by nocebo acquisition and extinction phases. During acquisition, high intensity thermal pain was paired with supposed activation of sham electrical stimuli (nocebo trials), whereas moderate pain was administered with inactive electrical stimulation (control trials). Nocebo hyperalgesia was induced in both groups (p < 0.001). Nocebo magnitudes and brain activations did not show significant differences between D-cycloserine and placebo. In acquisition and extinction, there were significantly increased activations bilaterally in the amygdala, ACC, and insula, during nocebo compared to control trials. Nocebo acquisition trials also showed increased vlPFC activation. Increased opercular activation differentiated nocebo-augmented pain aggravation from baseline pain. These results support the involvement of integrative cognitive-emotional processes in nocebo hyperalgesia.

Are metaphors embodied? The neural evidence

concepts can potentially be represented using metaphorical mappings to concrete domains. This view predicts that when linguistic metaphors are processed, they will invoke sensory-motor simulations. Here, I examine evidence from neuroimaging and lesion studies that addresses whether metaphors in language are embodied in this manner. Given the controversy in this area, I first outline some criteria by which the quality of neuroimaging and lesion studies might be evaluated. I then review studies of metaphors in various sensory-motor domains, such as action, motion, texture, taste, and time, and examine their strengths and weaknesses. Studies of idioms are evaluated next. I also address some neuroimaging studies that can speak to the question of metaphoric conceptual organization without explicit use of linguistic metaphors. I conclude that the weight of the evidence suggests that metaphors are indeed grounded in sensory-motor systems. The case of idioms is less clear, and I suggest that they might be grounded in a qualitatively different manner than metaphors at higher levels of the action hierarchy. While metaphors are unlikely to explain all aspects of abstract concept representation, for some specific abstract concepts, there is also nonlinguistic neural evidence for metaphoric conceptual organization.

How reliable are amygdala findings in psychopathy? A systematic review of MRI studies

The amygdala is a key component in predominant neural circuitry models of psychopathy. Yet, after two decades of neuroimaging research on psychopathy, the reproducibility of amygdala findings is questionable. We systematically reviewed MRI studies (81 of adults, 53 of juveniles) to determine the consistency of amygdala findings across studies, as well as within specific types of experimental tasks, community versus forensic populations, and the lowest- versus highest-powered studies. Three primary findings emerged. First, the majority of studies found null relationships between psychopathy and amygdala structure and function, even in the context of theoretically relevant tasks. Second, findings of reduced amygdala activity were more common in studies with low compared to high statistical power. Third, the majority of peak coordinates of reduced amygdala activity did not fall primarily within the anatomical bounds of the amygdala. Collectively, these findings demonstrate significant gaps in the empirical support for the theorized role of the amygdala in psychopathy and indicate the need for novel research perspectives and approaches in this field.

Neuroscout, a unified platform for generalizable and reproducible fMRI research

Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli-such as movies and narratives-allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.

Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference

Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting >20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate- compared with familywise error rate-controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.

Empirical facts from search for replicable associations between cortical thickness and psychometric variables in healthy adults

The study of associations between inter-individual differences in brain structure and behaviour has a long history in psychology and neuroscience. Many associations between psychometric data, particularly intelligence and personality measures and local variations of brain structure have been reported. While the impact of such reported associations often goes beyond scientific communities, resonating in the public mind, their replicability is rarely evidenced. Previously, we have shown that associations between psychometric measures and estimates of grey matter volume (GMV) result in rarely replicated findings across large samples of healthy adults. However, the question remains if these observations are at least partly linked to the multidetermined nature of the variations in GMV, particularly within samples with wide age-range. Therefore, here we extended those evaluations and empirically investigated the replicability of associations of a broad range of psychometric variables and cortical thickness in a large cohort of healthy young adults. In line with our observations with GMV, our current analyses revealed low likelihood of significant associations and their rare replication across independent samples. We here discuss the implications of these findings within the context of accumulating evidence of the general poor replicability of structural-brain-behaviour associations, and more broadly of the replication crisis.

CLEAN: Leveraging spatial autocorrelation in neuroimaging data in clusterwise inference

While clusterwise inference is a popular approach in neuroimaging that improves sensitivity, current methods do not account for explicit spatial autocorrelations because most use univariate test statistics to construct cluster-extent statistics. Failure to account for such dependencies could result in decreased reproducibility. To address methodological and computational challenges, we propose a new powerful and fast statistical method called CLEAN (Clusterwise inference Leveraging spatial Autocorrelations in Neuroimaging). CLEAN computes multivariate test statistics by modelling brain-wise spatial autocorrelations, constructs cluster-extent test statistics, and applies a refitting-free resampling approach to control false positives. We validate CLEAN using simulations and applications to the Human Connectome Project. This novel method provides a new direction in neuroimaging that paces with advances in high-resolution MRI data which contains a substantial amount of spatial autocorrelation.

A randomized trial shows dose-frequency and genotype may determine the therapeutic efficacy of intranasal oxytocin

BACKGROUND

The neuropeptide oxytocin is proposed as a promising therapy for social dysfunction by modulating amygdala-mediated social-emotional behavior. Although clinical trials report some benefits of chronic treatment, it is unclear whether efficacy may be influenced by dose frequency or genotype.

METHODS

In a randomized, double-blind, placebo-controlled pharmaco-functional magnetic resonance imaging trial (150 male subjects), we investigated acute and different chronic (every day or on alternate days for 5 days) intranasal oxytocin (24 international units) effects and oxytocin receptor genotype-mediated treatment sensitivity on amygdala responses to face emotions. We also investigated similar effects on resting-state functional connectivity between the amygdala and prefrontal cortex.

RESULTS

A single dose of oxytocin-reduced amygdala responses to all face emotions but for threatening (fear and anger) and happy faces, this effect was abolished after daily doses for 5 days but maintained by doses given every other day. The latter dose regime also enhanced associated anxious-arousal attenuation for fear faces. Oxytocin effects on reducing amygdala responses to face emotions only occurred in AA homozygotes of rs53576 and A carriers of rs2254298. The effects of oxytocin on resting-state functional connectivity were not influenced by either dose-frequency or receptor genotype.

CONCLUSIONS

Infrequent chronic oxytocin administration may be therapeutically most efficient and its anxiolytic neural and behavioral actions are highly genotype-dependent in males.

Oxytocinergic Modulation of Stress-Associated Amygdala-Hippocampus Pathways in Humans Is Mediated by Serotonergic Mechanisms

BACKGROUND

The hypothalamic neuropeptide oxytocin (OXT) may exert anxiolytic and stress-reducing actions via modulatory effects on amygdala circuits. Animal models and initial findings in humans suggest that some of these effects are mediated by interactions with other neurotransmitter systems, in particular the serotonin (5-HT) system. Against this background, the present pharmacological resting-state functional magnetic resonance imaging study aimed to determine whether effects of OXT on stress-associated amygdala intrinsic networks are mediated by 5-HT.

METHODS

We employed a randomized, placebo-controlled, double-blind parallel-group, pharmacological functional magnetic resonance imaging resting-state experiment with 4 treatment groups in n = 112 healthy male participants. Participants underwent a transient decrease in 5-HT signaling via acute tryptophan depletion (ATD) or a corresponding placebo-control protocol before the administration of intranasal OXT (24 IU) or placebo intranasal spray.

RESULTS

OXT and 5-HT modulation exerted interactive effects on the coupling of the left amygdala with the ipsilateral hippocampus and adjacent midbrain. OXT increased intrinsic coupling in this pathway, whereas this effect of OXT was significantly attenuated during transiently decreased central serotonergic signaling induced via acute tryptophan depletion. In the absence of OXT or 5-HT modulation, this pathway showed a trend for an association with self-reported stress perception in everyday life. No interactive effects were observed for the right amygdala.

CONCLUSIONS

Together, the findings provide the first evidence, to our knowledge, that the effects of OXT on stress-associated amygdala-hippocampal-midbrain pathways are critically mediated by the 5-HT system in humans.

Reward enhances connectivity between the ventral striatum and the default mode network

The default mode network (DMN) has been theorized to participate in a range of social, cognitive, and affective functions. Yet, previous accounts do not consider how the DMN contributes to other brain regions depending on psychological context, thus rendering our understanding of DMN function incomplete. We addressed this gap by applying a novel network-based psychophysiological interaction (nPPI) analysis to the reward task within the Human Connectome Project. We first focused on the task-evoked responses of the DMN and other networks involving the prefrontal cortex, including the executive control network (salience network) and the left and right frontoparietal networks. Consistent with a host of prior studies, the DMN exhibited a relative decrease in activation during the task, while the other networks exhibited a relative increase during the task. Next, we used nPPI analyses to assess whether these networks exhibit task-dependent changes in connectivity with other brain regions. Strikingly, we found that the experience of reward enhances task-dependent connectivity between the DMN and the ventral striatum, an effect that was specific to the DMN. Surprisingly, the strength of DMN-VS connectivity was correlated with personality characteristics relating to openness. Taken together, these results advance models of DMN by demonstrating how it contributes to other brain systems during task performance and how those contributions relate to individual differences.

Neural correlates of acute post-traumatic dissociation: a functional neuroimaging script-driven imagery study

BACKGROUND

Current neurobiological models of post-traumatic stress disorder (PTSD) assume excessive medial frontal activation and hypoactivation of cortico-limbic regions as neural markers of post-traumatic dissociation. Script-driven imagery is an established experimental paradigm that is used to study acute dissociative reactions during trauma exposure. However, there is a scarcity of experimental research investigating neural markers of dissociation; findings from existing script-driven neuroimaging studies are inconsistent and based on small sample sizes.

AIMS

The current aim was to identify the neural correlates of acute post-traumatic dissociation by employing the script-driven imagery paradigm in combination with functional magnetic resonance imaging.

METHOD

Functional neuroimaging data was acquired in 51 female patients with PTSD with a history of interpersonal childhood trauma. Blood-oxygen-level-dependent response during the traumatic (versus neutral) autobiographical memory recall was analysed, and the derived activation clusters were correlated with dissociation measures.

RESULTS

During trauma recall, enhanced activation in the cerebellum, occipital gyri, supramarginal gyrus and amygdala was identified. None of the derived clusters correlated significantly with dissociative symptoms, although patients reported increased levels of acute dissociation following the paradigm.

CONCLUSIONS

The present study is one of the largest functional magnetic resonance imaging investigations of dissociative neural biomarkers in patients with PTSD undergoing experimentally induced trauma confrontation to elicit symptom-specific brain reactivity. In light of the current reproducibility crisis prominent in neuroimaging research owing to costly and time-consuming data acquisition, the current (null) findings highlight the difficulty of extracting reliable neurobiological biomarkers for complex subjective experiences such as dissociation.

Typical resting state activity of the brain requires visual input during an early sensitive period

Sensory deprivation, following a total loss of one sensory modality e.g. vision, has been demonstrated to result in compensatory plasticity. It is yet not known to which extent neural changes, e.g. higher resting-state activity in visual areas (cross-modal plasticity) as a consequence of blindness, reverse, when sight is restored. Here, we used functional MRI to acquire blood oxygen level-dependent resting-state activity during an eyes open and an eyes closed state in congenital cataract-reversal individuals, developmental cataract-reversal individuals, congenitally permanently blind individuals and sighted controls. The amplitude of low frequency fluctuation of the blood oxygen level-dependent signal—a neural marker of spontaneous brain activity during rest—was analyzed. In accordance with previous reports, in normally sighted controls we observed an increase in amplitude of low-frequency fluctuation during rest with the eyes open compared with rest with eyes closed in visual association areas and in parietal cortex but a decrease in auditory and sensorimotor regions. In congenital cataract-reversal individuals, we found an increase of the amplitude of slow blood oxygen level-dependent fluctuations in visual cortex during rest with eyes open compared with rest with eyes closed too but this increase was larger in amplitude than in normally sighted controls. In contrast, congenital cataract-reversal individuals lagged a similar increase in parietal regions and did not show the typical decrease of amplitude of low-frequency fluctuation in auditory cortex. Congenitally blind individuals displayed an overall higher amplitude in slow blood oxygen level-dependent fluctuations in visual cortex compared with sighted individuals and compared with congenital cataract-reversal individuals in the eyes closed condition. Higher amplitude of low-frequency fluctuation in visual cortex of congenital cataract-reversal individuals than in normally sighted controls during eyes open might indicate an altered excitatory–inhibitory balance of visual neural circuits. By contrast, the lower parietal increase and the missing downregulation in auditory regions suggest a reduced influence of the visual system on multisensory and the other sensory systems after restoring sight in congenitally blind individuals. These results demonstrate a crucial dependence of visual and multisensory neural system functioning on visual experience during a sensitive phase in human brain development.

Differences in social brain function in autism spectrum disorder are linked to the serotonin transporter: A randomised placebo-controlled single-dose crossover trial

BACKGROUND

Alterations in the serotonergic control of brain pathways responsible for facial emotion processing in people with autism spectrum disorder (ASD) may be a target for intervention. However, the molecular underpinnings of autistic-neurotypical serotonergic differences are challenging to access in vivo. Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) has helped define molecular-enriched functional magnetic resonance imaging (fMRI) brain networks based on a priori information about the spatial distribution of neurochemical systems from available PET templates.

METHODS

We used REACT to estimate the dominant fMRI signal related to the serotonin (5-HT) transporter (SERT) distribution during processing of aversive facial emotion in adults with and without ASD. We first predicted a group difference in baseline (placebo) functioning of this system. We next used a single 20 mg oral dose of citalopram, a serotonin reuptake inhibitor, to test the hypothesis that network activity in people with and without ASD would respond differently to inhibition of SERT. To confirm the specificity of our findings, we also repeated the analysis with 5-HT1A, 5-HT1B, 5-HT2A and 5-HT4 receptor maps.

RESULTS

Using REACT with the SERT map, we found a baseline group difference in the SERT-enriched response to faces in the ventromedial prefrontal cortex. A single oral dose of citalopram 'shifted' the response in the ASD group towards the neurotypical baseline but did not alter response in the control group. Similar differences in SERT-enriched response were observed after controlling for other 5-HT maps.

CONCLUSIONS

Our findings suggest that the SERT-enriched functional network is dynamically different in ASD during processing of socially relevant stimuli. Whether this acute neurobiological response to citalopram in ASD translates to a clinical target will be an important next step.

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites¹ that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

Evaluation of thresholding methods for activation likelihood estimation meta-analysis via large-scale simulations

In recent neuroimaging studies, threshold‐free cluster enhancement (TFCE) gained popularity as a sophisticated thresholding method for statistical inference. It was shown to feature higher sensitivity than the frequently used approach of controlling the cluster‐level family‐wise error (cFWE) and it does not require setting a cluster‐forming threshold at voxel level. Here, we examined the applicability of TFCE to a widely used method for coordinate‐based neuroimaging meta‐analysis, Activation Likelihood Estimation (ALE), by means of large‐scale simulations. We created over 200,000 artificial meta‐analysis datasets by independently varying the total number of experiments included and the amount of spatial convergence across experiments. Next, we applied ALE to all datasets and compared the performance of TFCE to both voxel‐level and cluster‐level FWE correction approaches. All three multiple‐comparison correction methods yielded valid results, with only about 5% of the significant clusters being based on spurious convergence, which corresponds to the nominal level the methods were controlling for. On average, TFCE's sensitivity was comparable to that of cFWE correction, but it was slightly worse for a subset of parameter combinations, even after TFCE parameter optimization. cFWE yielded the largest significant clusters, closely followed by TFCE, while voxel‐level FWE correction yielded substantially smaller clusters, showcasing its high spatial specificity. Given that TFCE does not outperform the standard cFWE correction but is computationally much more expensive, we conclude that employing TFCE for ALE cannot be recommended to the general user.

Social cognitive network neuroscience

Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.

Longitudinal surface-based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS

Longitudinal fMRI studies hold great promise for the study of neurodegenerative diseases, development and aging, but realizing their full potential depends on extracting accurate fMRI-based measures of brain function and organization in individual subjects over time. This is especially true for studies of rare, heterogeneous and/or rapidly progressing neurodegenerative diseases. These often involve small samples with heterogeneous functional features, making traditional group-difference analyses of limited utility. One such disease is amyotrophic lateral sclerosis (ALS), a severe disease resulting in extreme loss of motor function and eventual death. Here, we use an advanced individualized task fMRI analysis approach to analyze a rich longitudinal dataset containing 190 hand clench fMRI scans from 16 ALS patients (78 scans) and 22 age-matched healthy controls (112 scans) Specifically, we adopt our cortical surface-based spatial Bayesian general linear model (GLM), which has high power and precision to detect activations in individual subjects, and propose a novel longitudinal extension to leverage information shared across visits. We perform all analyses in native surface space to preserve individua anatomical and functional features. Using mixed-effects models to subsequently study the relationship between size of activation and ALS disease progression, we observe for the first time an inverted U-shaped trajectory o motor activations: at relatively mild motor disability we observe enlarging activations, while at higher levels of motor disability we observe severely diminished activation, reflecting progression toward complete loss of motor function. We further observe distinct trajectories depending on clinical progression rate, with faster progressors exhibiting more extreme changes at an earlier stage of disability. These differential trajectories suggest that initial hyper-activation is likely attributable to loss of inhibitory neurons, rather than functional compensation as earlier assumed. These findings substantially advance scientific understanding of the ALS disease process. This study also provides the first real-world example of how surface-based spatial Bayesian analysis of task fMRI can further scientific understanding of neurodegenerative disease and other phenomena. The surface-based spatial Bayesian GLM is implemented in the BayesfMRI R package

Linking interindividual variability in brain structure to behaviour

What are the brain structural correlates of interindividual differences in behaviour? More than a decade ago, advances in structural MRI opened promising new avenues to address this question. The initial wave of research then progressively led to substantial conceptual and methodological shifts, and a replication crisis unveiled the limitations of traditional approaches, which involved searching for associations between local measurements of neuroanatomy and behavioural variables in small samples of healthy individuals. Given these methodological issues and growing scepticism regarding the idea of one-to-one mapping of psychological constructs to brain regions, new perspectives emerged. These not only embrace the multivariate nature of brain structure-behaviour relationships and promote generalizability but also embrace the representation of the relationships between brain structure and behavioural data by latent dimensions of interindividual variability. Here, we examine the past and present of the study of brain structure-behaviour associations in healthy populations and address current challenges and open questions for future investigations.

Clinical Effects of Repetitive Transcranial Magnetic Stimulation of the Motor Cortex Are Associated With Changes in Resting-State Functional Connectivity in Patients With Fibromyalgia Syndrome

In this double-blinded, sham-controlled, counterbalanced, and crossover study, we investigated the potential neuroplasticity underlying pain relief and daily function improvements following repetitive transcranial magnetic stimulation of the motor cortex (M1-rTMS) in fibromyalgia syndrome (FMS) patients. Specifically, we used magnetic resonance imaging (MRI) to examine changes in brain structural and resting-state functional connectivity (rsFC) that correlated with improvements in FMS symptomology following M1-rTMS. Twenty-seven women with FMS underwent real and sham treatment series, each consisting of 10 daily treatments of 10Hz M1-rTMS over 2 weeks, with a washout period in between. Before and after each series, participants underwent anatomical and resting-state functional MRI scans and questionnaire assessments of FMS-related clinical pain and functional and psychological burdens. The expected reductions in FMS-related symptomology following M1-rTMS occurred with the real treatment only and correlated with rsFC changes in brain areas associated with pain processing and modulation. Specifically, between the ventromedial prefrontal cortex and the M1 (t = -5.54, corrected P = .002), the amygdala and the posterior insula (t = 5.81, corrected P = .044), and the anterior and posterior insula (t = 6.01, corrected P = .029). Neither treatment significantly changed brain structure. Therefore, we provide the first evidence of an association between the acute clinical effects of M1-rTMS in FMS and functional alterations of brain areas that have a significant role in the experience of chronic pain. Structural changes could potentially occur over a more extended treatment period. PERSPECTIVE: We show that the neurophysiological mechanism of the improvement in fibromyalgia symptoms following active, but not sham, rTMS applied to M1 involves changes in resting-state functional connectivity in sensory, affective and cognitive pain processing brain areas, thus substantiating the essence of fibromyalgia syndrome as a treatable brain-based disorder.

Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups

The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not explicitly leverage the similarity of activation patterns among neighboring brain locations. As a result, it tends to produce noisy estimates and be underpowered to detect significant activations, particularly in individual subjects and small groups. A recently proposed alternative, a cortical surface-based spatial Bayesian GLM, leverages spatial dependencies among neighboring cortical vertices to produce more accurate estimates and areas of functional activation. The spatial Bayesian GLM can be applied to individual and group-level analysis. In this study, we assess the reliability and power of individual and group-average measures of task activation produced via the surface-based spatial Bayesian GLM. We analyze motor task data from 45 subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also extend the model to multi-run analysis and employ subject-specific cortical surfaces rather than surfaces inflated to a sphere for more accurate distance-based modeling. Results show that the surface-based spatial Bayesian GLM produces highly reliable activations in individual subjects and is powerful enough to detect trait-like functional topologies. Additionally, spatial Bayesian modeling enhances reliability of group-level analysis even in moderately sized samples (n=45). Notably, the power of the spatial Bayesian GLM to detect activations above a scientifically meaningful effect size is nearly invariant to sample size, exhibiting high power even in small samples (n=10). The spatial Bayesian GLM is computationally efficient in individuals and groups and is convenient to implement with the open-source BayesfMRI R package.

A New Functional Magnetic Resonance Imaging Localizer for Preoperative Language Mapping Using a Sentence Completion Task: Validity, Choice of Baseline Condition, and Test–Retest Reliability

To avoid post-neurosurgical language deficits, intraoperative mapping of the language function in the brain can be complemented with preoperative mapping with functional magnetic resonance imaging (fMRI). The validity of an fMRI “language localizer” paradigm crucially depends on the choice of an optimal language task and baseline condition. This study presents a new fMRI “language localizer” in Russian using overt sentence completion, a task that comprehensively engages the language function by involving both production and comprehension at the word and sentence level. The paradigm was validated in 18 neurologically healthy volunteers who participated in two scanning sessions, for estimating test–retest reliability. For the first time, two baseline conditions for the sentence completion task were compared. At the group level, the paradigm significantly activated both anterior and posterior language-related regions. Individual-level analysis showed that activation was elicited most consistently in the inferior frontal regions, followed by posterior temporal regions and the angular gyrus. Test–retest reliability of activation location, as measured by Dice coefficients, was moderate and thus comparable to previous studies. Test–retest reliability was higher in the frontal than temporo-parietal region and with the most liberal statistical thresholding compared to two more conservative thresholding methods. Lateralization indices were expectedly left-hemispheric, with greater lateralization in the frontal than temporo-parietal region, and showed moderate test-retest reliability. Finally, the pseudoword baseline elicited more extensive and more reliable activation, although the syllable baseline appears more feasible for future clinical use. Overall, the study demonstrated the validity and reliability of the sentence completion task for mapping the language function in the brain. The paradigm needs further validation in a clinical sample of neurosurgical patients. Additionally, the study contributes to general evidence on test–retest reliability of fMRI.

Examining the neural correlates of error awareness in a large fMRI study

Goal-directed behavior is dependent upon the ability to detect errors and implement appropriate posterror adjustments. Accordingly, several studies have explored the neural activity underlying error-monitoring processes, identifying the insula cortex as crucial for error awareness and reporting mixed findings with respect to the anterior cingulate cortex (ACC). Variable patterns of activation have previously been attributed to insufficient statistical power. We therefore sought to clarify the neural correlates of error awareness in a large event-related functional magnetic resonance imaging (fMRI) study. Four hundred and two healthy participants undertook the error awareness task, a motor Go/No-Go response inhibition paradigm in which participants were required to indicate their awareness of commission errors. Compared to unaware errors, aware errors were accompanied by significantly greater activity in a network of regions, including the insula cortex, supramarginal gyrus (SMG), and midline structures, such as the ACC and supplementary motor area (SMA). Error awareness activity was related to indices of task performance and dimensional measures of psychopathology in selected regions, including the insula, SMG, and SMA. Taken together, we identified a robust and reliable neural network associated with error awareness.

A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus

Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

Effects of mindset on hormonal responding, neural representations, subjective experience and intake

A person can alternate between food-related mindsets, which in turn may depend on one's emotional state or situation. Being in a certain mindset can influence food-related thoughts, but interestingly it might also affect eating-related physiological responses. The current study investigates the influence of an induced 'loss of control' mindset as compared to an 'in control' mindset on hormonal, neural and behavioural responses to chocolate stimuli. Mindsets were induced by having female chocolate lovers view a short movie during two sessions in a within-subjects design. Neural responses to visual chocolate stimuli were measured using an ultra-high field (7T) scanner. Momentary ghrelin and glucagon-like peptide 1 (GLP-1) levels were determined on five moments and were simultaneously assessed with self-reports on perceptions of chocolate craving, hunger and feelings of control. Furthermore, chocolate intake was measured using a bogus chocolate taste test. It was hypothesized that the loss of control mindset would lead to hormonal, neural and behavioural responses that prepare for ongoing food intake, even after eating, while the control mindset would lead to responses reflecting satiety. Results show that neural activity in the mesocorticolimbic system was stronger for chocolate stimuli than for neutral stimuli and that ghrelin and GLP-1 levels responded to food intake, irrespective of mindset. Self-reported craving and actual chocolate intake were affected by mindset, in that cravings and intake were higher with a loss of control mindset than with a control mindset. Interestingly, these findings suggest that physiology on the one hand (hormonal and neural responses) and behavior and subjective experience (food intake and craving) on the other hand are not in sync, are not equally affected by mindset.

Large-scale functional brain networks of maladaptive childhood aggression identified by connectome-based predictive modeling

Disruptions in frontoparietal networks supporting emotion regulation have been long implicated in maladaptive childhood aggression. However, the association of connectivity between large-scale functional networks with aggressive behavior has not been tested. The present study examined whether the functional organization of the connectome predicts severity of aggression in children. This cross-sectional study included a transdiagnostic sample of 100 children with aggressive behavior (27 females) and 29 healthy controls without aggression or psychiatric disorders (13 females). Severity of aggression was indexed by the total score on the parent-rated Reactive-Proactive Aggression Questionnaire. During fMRI, participants completed a face emotion perception task of fearful and calm faces. Connectome-based predictive modeling with internal cross-validation was conducted to identify brain networks that predicted aggression severity. The replication and generalizability of the aggression predictive model was then tested in an independent sample of children from the Adolescent Brain Cognitive Development (ABCD) study. Connectivity predictive of aggression was identified within and between networks implicated in cognitive control (medial-frontal, frontoparietal), social functioning (default mode, salience), and emotion processing (subcortical, sensorimotor) (r = 0.31, RMSE = 9.05, p = 0.005). Out-of-sample replication (p < 0.002) and generalization (p = 0.007) of findings predicting aggression from the functional connectome was demonstrated in an independent sample of children from the ABCD study (n = 1791; n = 1701). Individual differences in large-scale functional networks contribute to variability in maladaptive aggression in children with psychiatric disorders. Linking these individual differences in the connectome to variation in behavioral phenotypes will advance identification of neural biomarkers of maladaptive childhood aggression to inform targeted treatments.

Prefrontal and striatal dopamine D2/D3 receptors correlate with fMRI BOLD activation during stopping

D2-like dopamine receptors in animals and humans have been shown to be linked to impulsive behaviors that are highly relevant for several psychiatric disorders. Here, we investigate the relationship between the fronto-striatal D2/D3 dopamine receptor availability and response inhibition in a selected population of healthy OPRM1 G-allele carriers. Twenty-two participants successively underwent blood-oxygen level dependent functional magnetic resonance imaging (fMRI) while performing a stop-signal task and a separate positron emission tomography (PET) scan. Striatal and extrastriatal D2/D3 dopamine receptor availability was measured using the radiotracer [18F]fallypride. Caudate D2/D3 dopamine receptor availability positively correlated with stopping-related fronto-striatal fMRI activation. In addition, right prefrontal D2/D3 dopamine receptor availability correlated positively with stopping-related striatal fMRI BOLD signal. Our study partially replicates previous findings on correlations between striatal D2/D3 dopamine receptor availability and response inhibition in a population selected for its genetic determination of dopamine response to alcohol and as a modulator of impulse control via the endogenous opioid system. We confirm the important role of D2/D3 dopamine receptor availability in the fronto-striatal neural circuit for response inhibition. Moreover, we extend previous findings suggesting that dopamine receptor availability in the right inferior frontal cortex, a crucial region of the stopping network, is also strongly associated with stopping-related striatal fMRI activity in healthy OPRM1 G-allele carriers.

A Novel Method to Use Coordinate Based Meta-Analysis to Determine a Prior Distribution for Voxelwise Bayesian Second-Level fMRI Analysis

Previous research showed that employing results from meta-analyses of relevant previous fMRI studies can improve the performance of voxelwise Bayesian second-level fMRI analysis. In this process, prior distributions for Bayesian analysis can be determined by information acquired from the meta-analyses. However, only image-based meta-analysis, which is not widely accessible to fMRI researchers due to the lack of shared statistical images, was tested in the previous study, so the applicability of the prior determination method proposed by the previous study might be limited. In the present study, whether determining prior distributions based on coordinate-based meta-analysis, which is widely accessible to researchers, can also improve the performance of Bayesian analysis, was examined. Three different types of coordinate-based meta-analyses, BrainMap and Ginger ALE, and NeuroQuery, were tested as information sources for prior determination. Five different datasets addressing three task conditions, i.e., working memory, speech, and face processing, were analyzed via Bayesian analysis with a meta-analysis informed prior distribution, Bayesian analysis with a default Cauchy prior adjusted for multiple comparisons, and frequentist analysis with familywise error correction. The findings from the aforementioned analyses suggest that use of coordinate-based meta-analysis also significantly enhanced performance of Bayesian analysis as did image-based meta-analysis.

Ventral Striatal-Hippocampus Coupling During Reward Processing as a Stratification Biomarker for Psychotic Disorders

BACKGROUND

Altered ventral striatal (vST) activation to reward expectancy is a well-established intermediate phenotype for psychiatric disorders, specifically schizophrenia (SZ). Preclinical research suggests that striatal alterations are related to a reduced inhibition by the hippocampal formation, but its role in human transdiagnostic reward-network dysfunctions is not well understood.

METHODS

We performed functional magnetic resonance imaging during reward processing in 728 individuals including healthy control subjects (n = 396), patients (SZ: n = 46; bipolar disorder: n = 45; major depressive disorder: n = 60), and unaffected first-degree relatives (SZ: n = 46; bipolar disorder: n = 50; major depressive disorder: n = 85). We assessed disorder-specific differences in functional vST-hippocampus coupling and transdiagnostic associations with dimensional measures of positive, negative, and cognitive symptoms. We also probed the genetic underpinning using polygenic risk scores for SZ in a subset of healthy participants (n = 295).

RESULTS

Functional vST-hippocampus coupling was 1) reduced in patients with SZ and bipolar disorder (p_FWE < .05, small-volume corrected [SVC]); 2) associated transdiagnostically to dimensional measures of positive (p_FWE = .01, SVC) and cognitive (p_FWE = .02, SVC), but not negative, (p_FWE > .05, SVC) symptoms; and 3) reduced in first-degree relatives of patients with SZ (p_FWE = .017, SVC) and linked to the genetic risk for SZ in healthy participants (p = .035).

CONCLUSIONS

We provide evidence that reduced vST-hippocampus coupling during reward processing is an endophenotype for SZ linked to positive and cognitive symptoms, supporting current preclinical models of the emergence of psychosis. Moreover, our data indicate that vST-hippocampus coupling is familial and linked to polygenic scores for SZ, supporting the use of this measure as an intermediate phenotype for psychotic disorders.

fMRI replicability during emotional scene viewing: Functional regions and sample size

Recent findings have questioned the replicability of functional magnetic resonance imaging (fMRI) in the study of affective processing, reporting low replicability of emotional enhancement during a face-matching task. However, poor replicability may instead reflect a lack of emotional engagement for face matching. In the current study, replicability of emotional enhancement was tested in a large (N = 160) sample when emotional engagement was assessed during pleasant, neutral, and unpleasant picture viewing, which reliably engages affective reactions in both the brain and the body. Replicability was computed using a subsampling technique, in which random sets of subjects of different sample sizes (N = 20, 40, 60, 80) were selected from the entire dataset, and replicability of emotional enhancement for peaks, clusters, and voxels were averaged across 500 permutations for each sample size. Consistent with previous findings, fMRI replicability increased with increasing sample size. On the other hand, even with relatively small samples, fMRI replicability for peaks, clusters, and voxels during emotional, compared to neutral, scene viewing was good to excellent. Importantly, replicability varied in different brain regions, with excellent replicability at both the cluster and peak level with an N of 40, at the most conservative threshold (p < .001), in the amygdala and the visual cortex. The data argue against general recommendations regarding sample size in fMRI studies of emotion, suggesting instead that degree of replicability depends on successful emotional engagement in task-related brain regions.

Effect sizes of associations between neuroimaging measures and affective symptoms: A meta-analysis

BACKGROUND

The utility of brain-based biomarkers for psychiatric disorders hinges among other factors on their ability to explain a significant portion of the phenotypic variance. In particular, many small scale studies have been unable to arbitrate whether structural or functional magnetic resonance imaging has potential to be a biological marker for these disorders.

METHODS

This study conducted a meta-analysis to examine the relationship between study power and published effect sizes for the relationship between affective symptoms and structural or functional magnetic resonance imaging measures. The current analyses are based on 821 brain-affective symptom association effect sizes derived from 120 publications, which employed a univariate region-of-interest approach.

RESULTS

For self-assessed affective symptoms published brain imaging measures accounted for on average 8% (confidence interval: 1.6%-23%) of between-subject variation. This average effect size was based mostly on studies with small sample sizes, which have likely led to inflation of these effect size estimates.

CONCLUSIONS

These findings support the conclusion that brain imaging measures currently account for a smaller proportion of the interindividual variance in affective symptoms than has been previously reported. The current findings support the need for both large-sample clinical studies and new statistical and theoretical models to more robustly capture systematic variance of brain-affective symptom relationships.

The Central Inflammatory Network: A Hypothalamic fMRI Study of Experimental Endotoxemia in Humans

INTRODUCTION

Inflammation is a mechanism of the immune system that is part of the reaction to pathogens or injury. The central nervous system closely regulates inflammation via neuroendocrine or direct neuroimmune mechanisms, but our current knowledge of the underlying circuitry is limited. Therefore, we aimed to identify hypothalamic centres involved in sensing or modulating inflammation and to study their association with known large-scale brain networks.

METHODS

Using high-resolution functional magnetic resonance imaging (fMRI), we recorded brain activity in healthy male subjects undergoing experimental inflammation from intravenous endotoxin. Four fMRI runs covered key phases of the developing inflammation: pre-inflammatory baseline, onset of endotoxemia, onset of pro-inflammatory cytokinemia, and peak of pro-inflammatory cytokinemia. Using masked independent component analysis, we identified functionally homogeneous subregions of the hypothalamus, which were further tested for changes in functional connectivity during inflammation and for temporal correlation with tumour necrosis factor and adrenocorticotropic hormone serum levels. We then studied the connection of these inflammation-associated hypothalamic subregions with known large-scale brain networks.

RESULTS

Our results show that there are at least 6 hypothalamic subregions associated with inflammation in humans including the paraventricular nucleus, supraoptic nucleus, dorsomedial hypothalamus, bed nucleus of the stria terminalis, lateral hypothalamic area, and supramammillary nucleus. They are functionally embedded in at least 3 different large-scale brain networks, namely a medial frontoparietal network, an occipital-pericentral network, and a midcingulo-insular network.

CONCLUSION

Measuring how the hypothalamus detects or modulates systemic inflammation is a first step to understand central nervous immunomodulation.

Neural correlates of compassion - An integrative systematic review

Compassion is a psychological construct that has received increasing attention in recent years. Even though a lot of work has been done to identify neural correlates of empathy across studies, such work has not been properly done on neural correlates of compassion. Therefore, the aim was to systematically review the literature on neural correlates of compassion. We have searched through PsycINFO, PubMed and Web of Science for relevant articles published between 1985 and 2020. We included the studies (n = 35) examining the relationship between brain structure or function and compassion. Screening was performed by two authors, between whom a level of agreement was calculated. The quality of the studies was assessed by measures used in other studies as well by measures specific for our study aims. This study was conducted under PRISMA guidelines. Our analysis revealed that the most frequent neural associations with compassion across all analysed studies can be found in the orbital part of the left inferior frontal gyrus, in the right cerebellum, the bilateral middle temporal gyrus, in the bilateral insula and the right caudate nucleus. Our findings suggest that people displaying a lower compassion tend to have either lower neural activity or a grey matter volume in neural areas associated with reward.

Distributed and Multifaceted Effects of Threat and Safety

In the present fMRI study, we examined how anxious apprehension is processed in the human brain. A central goal of the study was to test the prediction that a subset of brain regions would exhibit sustained response profiles during threat periods, including the anterior insula, a region implicated in anxiety disorders. A second important goal was to evaluate the responses in the amygdala and the bed nucleus of the stria terminals, regions that have been suggested to be involved in more transient and sustained threat, respectively. A total of 109 participants performed an experiment in which they encountered "threat" or "safe" trials lasting approximately 16 sec. During the former, they experienced zero to three highly unpleasant electrical stimulations, whereas in the latter, they experienced zero to three benign electrical stimulations (not perceived as unpleasant). The timing of the stimulation during trials was randomized, and as some trials contained no stimulation, stimulation delivery was uncertain. We contrasted responses during threat and safe trials that did not contain electrical stimulation, but only the potential that unpleasant (threat) or benign (safe) stimulation could occur. We employed Bayesian multilevel analysis to contrast responses to threat and safe trials in 85 brain regions implicated in threat processing. Our results revealed that the effect of anxious apprehension is distributed across the brain and that the temporal evolution of the responses is quite varied, including more transient and more sustained profiles, as well as signal increases and decreases with threat.

Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference

Classical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to ‘null effect’ assessment focusing on the Bayesian parameter inference (BPI). Although Bayesian methods have been theoretically elaborated and implemented in common neuroimaging software packages, they are not widely used for ‘null effect’ assessment. BPI considers the posterior probability of finding the effect within or outside the region of practical equivalence to the null value. It can be used to find both ‘activated/deactivated’ and ‘not activated’ voxels or to indicate that the obtained data are not sufficient using a single decision rule. It also allows to evaluate the data as the sample size increases and decide to stop the experiment if the obtained data are sufficient to make a confident inference. To demonstrate the advantages of using BPI for fMRI data group analysis, we compare it with classical null hypothesis significance testing on empirical data. We also use simulated data to show how BPI performs under different effect sizes, noise levels, noise distributions and sample sizes. Finally, we consider the problem of defining the region of practical equivalence for BPI and discuss possible applications of BPI in fMRI studies. To facilitate ‘null effect’ assessment for fMRI practitioners, we provide Statistical Parametric Mapping 12 based toolbox for Bayesian inference.

Brain-wide inferiority and equivalence tests in fMRI group analyses: Selected applications

Null hypothesis significance testing is the major statistical procedure in fMRI, but provides only a rather limited picture of the effects in a data set. When sample size and power is low relying only on strict significance testing may lead to a host of false negative findings. In contrast, with very large data sets virtually every voxel might become significant. It is thus desirable to complement significance testing with procedures like inferiority and equivalence tests that allow to formally compare effect sizes within and between data sets and offer novel approaches to obtain insight into fMRI data. The major component of these tests are estimates of standardized effect sizes and their confidence intervals. Here, we show how Hedges' g, the bias corrected version of Cohen's d, and its confidence interval can be obtained from SPM t maps. We then demonstrate how these values can be used to evaluate whether nonsignificant effects are really statistically smaller than significant effects to obtain “regions of undecidability” within a data set, and to test for the replicability and lateralization of effects. This method allows the analysis of fMRI data beyond point estimates enabling researchers to take measurement uncertainty into account when interpreting their findings.

Enhanced design matrix for task-related fMRI data analysis

In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology.

Region-of-interest analysis approaches in neuroimaging studies of body ownership: An activation likelihood estimation meta-analysis

How do we feel that we own our body? By manipulating the integration of multisensory signals and creating the illusory experience of owning external body parts and entire bodies, researchers have investigated the neurofunctional correlates of body ownership. Recent attempts to synthesize the neuroimaging literature of body ownership through meta-analysis have shown partly inconsistent results. A large proportion of functional magnetic resonance imaging (fMRI) findings on body ownership include analyses based on regions of interest (ROIs). This approach can produce inflated findings when results are synthesized in meta-analyses. We conducted a systematic search of the fMRI literature of ownership of body parts and entire bodies. Three activation likelihood estimation (ALE) meta-analyses were conducted, testing the impact of including ROI-based findings. When both whole-brain and ROI-based results were included, frontal and posterior parietal multisensory areas were associated with body ownership. When only ROI-based results were included, larger areas of the frontal and posterior parietal cortices and the middle occipital gyrus were associated with body ownership. A whole-brain meta-analysis, excluding ROI-based results, found no significant convergence of activation across the brain. These findings highlight the difficulty of quantitatively synthesizing a neuroimaging field where a large part of the literature is based on findings from ROI-based analyses. We discuss these findings in the light of current practices within this field of research and highlight current problems of meta-analytic approaches of body ownership. We recommend the sharing of unthresholded data as a means to facilitate future meta-analyses of the neuroimaging literature of body ownership.

Personality network neuroscience: Promises and challenges on the way toward a unifying framework of individual variability

We propose that the application of network theory to established psychological personality conceptions has great potential to advance a biologically plausible model of human personality. Stable behavioral tendencies are conceived as personality “traits.” Such traits demonstrate considerable variability between individuals, and extreme expressions represent risk factors for psychological disorders. Although the psychometric assessment of personality has more than hundred years tradition, it is not yet clear whether traits indeed represent “biophysical entities” with specific and dissociable neural substrates. For instance, it is an open question whether there exists a correspondence between the multilayer structure of psychometrically derived personality factors and the organizational properties of traitlike brain systems. After a short introduction into fundamental personality conceptions, this article will point out how network neuroscience can enhance our understanding about human personality. We will examine the importance of intrinsic (task-independent) brain connectivity networks and show means to link brain features to stable behavioral tendencies. Questions and challenges arising from each discipline itself and their combination are discussed and potential solutions are developed. We close by outlining future trends and by discussing how further developments of network neuroscience can be applied to personality research.

Acupuncture Treatment Associated with Functional Connectivity Changes in Primary Dysmenorrhea: A Resting State fMRI Study

Primary dysmenorrhea (PDM) is the most commonly encountered gynecological problem in reproductive-age women. Acupuncture has been suggested as an effective treatment of PDM that may modulate descending pain modulation systems. In the present study, we used resting-state functional magnetic resonance imaging to investigate possible changes in descending pain modulation systems after acupuncture treatment in women with PDM. Thirty-four right-handed adult women with PDM participated in this randomized, single-blinded, sham-controlled study. Each patient was randomly allocated to an 8-week verum or sham acupuncture intervention on the bilateral Sanyinjiao (SP6). Resting-state functional magnetic resonance imaging was conducted before, during, and after the intervention to measure the spontaneous activity in brain. After the 8-week intervention, both verum and sham groups reported decreased menstrual pain. However, the cessation of decreased functional connectivity (FC) between periaqueductal gray matter and the regions associated with affective pain modulation and attention-related pain modulation were found in the verum but not in the sham group after the 8-week intervention. More decreased FC has been found in the region associated with non-specific effects of acupuncture intervention after the early stage of acupuncture intervention. These results indicated that verum acupuncture may intercept the altered FC in descending pain modulation systems in PDM.

An fMRI investigation of the intention-outcome interactions in second- and third-party punishment

Second-party punishment (SPP) and third-party punishment (TPP) are two basic forms of costly punishment that play an essential role in maintaining social orders. Despite scientific breakthroughs in understanding that costly punishment is driven by an integration of the wrongdoers' intention and the outcome of their actions, so far, few studies have compared the neurocognitive processes associated with the intention-outcome integration between SPP and TPP. Here, we combined economic exchange games measuring SPP and TPP with functional magnetic resonance imaging (fMRI) to compare the neuropsychological architectures underlying the intention-outcome integration during one-shot interactions with anonymous partners across four types of norm violations (no norm, accidental, attempted, and intentional violations). Our behavioral findings showed that third-parties punished only attempted norm violations less frequently than second-parties. Our neuroimaging findings revealed higher activities in the right temporoparietal junction (TPJ) and dorsolateral prefrontal cortex (dlPFC) for attempted norm violations during TPP relative to SPP; more activities in these regions with less punishment frequency; and enhancement of functional connectivity of the right TPJ with the right dlPFC and dorsomedial PFC. Our findings demonstrated specific psychological and neural mechanisms of intention-outcome interactions between SPP and TPP -helping to unravel the complex neurocognitive processes of costly punishment.