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Resting-state functional connectivity has generated great hopes as a potential brain biomarker for improving prevention, diagnosis, and treatment in psychiatry. This neuroimaging protocol can routinely be performed by patients and does not depend on the specificities of a task. Thus, it seems ideal for big data approaches that require aggregating data across multiple studies and sites. However, technical variability, diverging data analysis approaches, and differences in data acquisition protocols might introduce heterogeneity to the aggregated data. Besides these technical aspects, the psychological state of participants might also contribute to heterogeneity. In healthy participants, studies have shown that behavioral tasks can influence resting-state measures, but such effects have not yet been reported in clinical populations. Here, we fill this knowledge gap by comparing resting-state functional connectivity before and after clinically relevant tasks in two clinical conditions, namely substance use disorders and phobias. The tasks consisted of viewing craving-inducing and spider anxiety provoking pictures that are frequently used in cue-reactivity studies and exposure therapy. We found distinct pre- vs. post-task resting-state connectivity differences in each group, as well as decreased thalamo-cortical and increased intra-thalamic connectivity which might be associated with decreased vigilance in both groups. Notably, the pre- vs. post-task thalamus-amygdala connectivity change within a patient cohort seems more pronounced than the difference of that connection between the smoker vs. phobia clinical trait. Our results confirm that resting-state measures can be strongly influenced by changes in psychological states that need to be taken into account when pooling resting-state scans for clinical biomarker detection. This demands that resting-state datasets should include a complete description of the experimental design, especially when a task preceded data collection.

Background Several fMRI studies found hyperactivity in the hippocampus during pattern separation tasks in patients with Mild Cognitive Impairment (MCI; a prodromal stage of Alzheimer’s disease). This was associated with memory deficits, subsequent cognitive decline, and faster clinical progression. A reduction of hippocampal hyperactivity with an antiepileptic drug improved memory performance. Pharmacological interventions, however, entail the risk of side effects. An alternative approach may be real-time fMRI neurofeedback, during which individuals learn to control region-specific brain activity. In the current project we aim to test the potential of neurofeedback to reduce hippocampal hyperactivity and thereby improve memory performance. Methods In a single-blind parallel-group study, we will randomize n = 84 individuals (n = 42 patients with MCI, n = 42 healthy elderly volunteers) to one of two groups receiving feedback from either the hippocampus or a functionally independent region. Percent signal change of the hemodynamic response within the respective target region will be displayed to the participant with a thermometer icon. We hypothesize that only feedback from the hippocampus will decrease hippocampal hyperactivity during pattern separation and thereby improve memory performance. Discussion Results of this study will reveal whether real-time fMRI neurofeedback is able to reduce hippocampal hyperactivity and thereby improve memory performance. In addition, the results of this study may identify predictors of successful neurofeedback as well as the most successful regulation strategies. Trial registration The study has been registered with clinicaltrials.gov on the 16th of July 2019 (trial identifier: NCT04020744).

Comparative neuroimaging allows for the identification of similarities and differences between species. It provides an important and promising avenue, to answer questions about the evolutionary origins of the brain’s organization, in terms of both structure and function. Dog fMRI has recently become one particularly promising and increasingly used approach to study brain function and coevolution. In dog neuroimaging, image acquisition has so far been mostly performed with coils originally developed for use in human MRI. Since such coils have been tailored to human anatomy, their sensitivity and data quality is likely not optimal for dog MRI. Therefore, we developed a multi-channel receive coil (K9 coil) tailored for high-resolution functional imaging in canines, optimized for dog cranial anatomy. In this paper we report structural (n = 9) as well as functional imaging data (resting-state, n = 6; simple visual paradigm, n = 9) collected with the K9 coil in comparison to reference data collected with a human knee coil. Our results show that the K9 coil significantly outperforms the human knee coil, improving the signal-to-noise ratio across the imaging modalities. We noted increases of roughly 45% signal-to-noise in the structural and functional domain. In terms of translation to functional fMRI data collected in a visual flickering checkerboard paradigm, group-level analyses show that the K9 coil performs better than the knee coil as well. These findings demonstrate how hardware improvements may be instrumental in driving data quality, and thus, quality of imaging results, for dog-human comparative neuroimaging.Significance StatementComparative neuroimaging is a powerful avenue to discover evolutionary mechanisms at the brain level. However, data quality is a major constraint in non-human functional magnetic resonance imaging. We describe a novel canine head coil for magnetic resonance imaging, designed specifically for dog cranial anatomy. Data quality performance and improvements over previously used human knee coils are described quantitatively. In brief, the canine coil improved signal quality substantially across both structural and functional imaging domains, with strongest improvements noted on the cortical surface.

Pain habituation is associated with a decrease of activation in brain areas related to pain perception. However, little is known about the specificity of these decreases to pain, as habituation has also been described for other responses like spinal reflexes and other sensory responses. Thus, it might be hypothesized that previously reported reductions in activation are not specifically related to pain habituation. For this reason, we performed a 3 T fMRI study using either painful or non-painful electrical stimulation via an electrode attached to the back of the left hand. Contrasting painful vs. non-painful stimulation revealed significant activation clusters in regions well-known to be related to pain processing, such as bilateral anterior and posterior insula, primary/secondary sensory cortices (S1/S2) and anterior midcingulate cortex (aMCC). Importantly, our results show distinct habituation patterns for painful (in aMCC) and non-painful (contralateral claustrum) stimulation, while similar habituation for both types of stimulation was identified in bilateral inferior frontal gyrus (IFG) and contralateral S2. Our findings thus distinguish a general habituation in somatosensory processing (S2) and reduced attention (IFG) from specific pain and non-pain related habituation effects where pain-specific habituation effects within the aMCC highlight a change in affective pain perception.

Cooperation and mutual trust are essential in our society, yet not everybody is trustworthy. In this fMRI study, 62 healthy volunteers performed a repeated trust game, placing trust in a trustworthy or an untrustworthy player. We found that the central amygdala was active during trust behavior planning while the basolateral amygdala was active during outcome evaluation. When planning the trust behavior, central and basolateral amygdala activation was stronger for the untrustworthy player compared to the trustworthy player but only in participants who actually learned to differentiate the trustworthiness of the players. Independent of learning success, nucleus accumbens encoded whether trust was reciprocated. This suggests that learning whom to trust is not related to reward processing in the nucleus accumbens, but rather to engagement of the amygdala. Our study overcomes major empirical gaps between animal models and human neuroimaging and shows how different subnuclei of the amygdala and connected areas orchestrate learning to form different subjective trustworthiness beliefs about others and guide trust choice behavior., Sladky & Riva et al. investigated how human allocation of trust depends on brain activity of the amygdala, a core brain structure for social cognition. They found dissociated activation between the amygdala and the nucleus accumbens, the former coding for trust assignation, and the latter coding for trust reciprocation.

INTRODUCTION The importance of the amygdala - medial orbitofrontal cortex (OFC) network during processing of emotional stimuli, emotional faces in particular, is well established. This premise is supported by converging evidence from animal models, human neuroanatomical results, and neuroimaging studies. However, there is missing evidence from human brain connectivity studies that the OFC and no other prefrontal brain areas such as the DLPFC or VLPFC are responsible for amygdala regulation in the functional context of emotional face stimuli. METHODS Dynamic causal modeling of ultra-high field functional MRI data acquired at 7 Tesla in 38 healthy subjects and a well-established paradigm for emotional face processing were used to assess the central role of the OFC in order to provide empirical validation for the assumed network architecture. RESULTS Using Bayesian model selection, it is demonstrated that indeed the OFC, and not the VLPFC and the DLPFC, down regulates amygdala activation during the emotion discrimination task. Additionally, Bayesian model averaging group results were rigorously tested using bootstrapping, further corroborating these findings and providing an estimator for robustness and optimal sample sizes. DISCUSSION While it is true that VLPFC and DLPFC are relevant for the processing of emotional faces and are connected to the OFC, the OFC appears to be a central hub for prefrontal-amygdala interaction.

Empathy is significantly influenced by the identification of others’ emotions. In a recent study, we have found increased activation in the anterior insular cortex (aIns) that could be attributed to affect sharing rather than perceptual saliency, when seeing another person genuinely experiencing pain as opposed to merely acting to be in pain. This study further revealed effective connectivity between aIns and the right supramarginal gyrus (rSMG) to track what another person really feels. In the present study, we used a similar paradigm to investigate the corresponding neural signatures in the domain of empathy for disgust - with participants seeing others genuinely sniffing unpleasant odors as compared to pretending to smell something disgusting. Consistent with the previous findings on pain, we found stronger activations in aIns associated with affect sharing for genuine disgust compared with pretended disgust. However, instead of rSMG we found engagement of the olfactory cortex. Using dynamic causal modeling (DCM), we estimated the neural dynamics of aIns and the olfactory cortex between the genuine and pretended conditions. This revealed an increased excitatory modulatory effect for genuine disgust compared to pretended disgust. For genuine disgust only, brain-to-behavior regression analyses highlighted a link between the observed modulatory effect and the perspective-taking empathic trait. Altogether, the current findings complement and expand our previous work, by showing that perceptual saliency alone does not explain responses in the insular cortex. Moreover, it reveals that different brain networks are implicated in a modality-specific way when sharing the affective experiences associated with pain vs. disgust. Significant statement Others’ feelings influence our own feelings, no matter whether these feelings are genuine or merely pretended. In our previous study, we have shown the interaction of the region related to affect sharing and affective self-other distinction allowed us to track others’ genuine experiences of pain vs. pretended pain. Here, we adopted a similar paradigm but in the domain of empathy for disgust. Results indicate both similar and distinct brain networks that are engaged when sharing others’ affective experiences of pain and disgust. This suggests that we could clearly distinguish others’ feelings of genuinely aversive experiences as compared to pretended affects, while domain-specific neural underpinnings might play a role in response to different affective experiences related to pain vs. disgust.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing. publishedVersion

The aim of this study was to investigate the effect of acute dopamine agonistic and antagonistic manipulation on the visual-cue induced blood oxygen level-dependent signal response in healthy volunteers. Seventeen healthy volunteers in a double-blind placebo-controlled cross-over design received either a dopamine antagonist, agonist or placebo and underwent functional magnetic resonance imaging. Using classical inference and Bayesian statistics, we found no effect of dopaminergic modulation on properties of visual-cue induced blood oxygen level-dependent signals in the visual cortex, particularly on distinct properties of the haemodynamic response function (amplitude, time-to-peak and width). Dopamine-related effects modulating the neurovascular coupling in the visual cortex might be negligible when measured via functional magnetic resonance imaging.

Creativity is a sine qua non ability for almost all aspects of everyday life. Although very profound behavioural models were provided by 21st century psychologists, the neural correlates of these personality features associated with creativity are largely unknown. Recent models suggest strong relationships between dopamine release and various creative skills. Herein, we employed functional connectivity analyses of resting-state functional magnetic imaging data in order to shed light on these neural underpinnings of creative aspects. For improved sensitivity, we performed the study at ultra-high magnetic field (7 T). Seed regions were defined based on subcortical (ventral tegmental area/substantia nigra, nucleus caudatus) activation foci of a remote associates task (RAT). In addition, bilateral PCC was used as seed region to examine the default-mode network. Network strength across subjects was regressed against a battery of psychological variables related to creativity. Dopaminergic network variations turned out to be indicative for individual differences in creative traits. In this regard, the caudate network showed stronger connectivity in individuals with higher extraversion measures, while connectivity with the midbrain network was found increased with higher ideational behaviour and emotional stability.

Human societies are built on cooperation and mutual trust, but not everybody is trustworthy. Research on rodents suggests an essential role of the basolateral amygdala (BLA) in learning from social experiences (Hernandez-Lallement J et al., 2016), which was also confirmed in human subjects with selective bilateral BLA damage as they failed to adapt their trust behavior towards trustworthy vs. untrustworthy interaction partners (Rosenberger LA et al., 2019). However, neuroimaging in neurotypical populations did not consistently report involvement of the amygdala in trust behavior. This might be explained by the difficulty of differentiating between amygdala’s structurally and functionally different subnuclei, i.e., the BLA and central amygdala (CeA), which have even antagonistic features particularly in trust behavior (van Honk J et al., 2013). Here, we used fMRI of the amygdala subnuclei of neurotypical adults (n=31f/31m) engaging in the repeated trust game. Our data show that both the BLA and the CeA play a role and indeed differentially: While the BLA was most active when obtaining feedback on whether invested trust had been reciprocated or not, the CeA was most active when subjects were preparing their next trust decision. In the latter phase, improved learning was associated with higher activation differences in response to untrustworthy vs. trustworthy trustees, in both BLA and CeA. Our data not only translate to rodent models and support our earlier findings in BLA-damaged subjects, but also show the specific contributions of other brain structures in the amygdala-centered network in learning whom to trust, and better not to trust.SIGNIFICANCE STATEMENTIn this fMRI study, the central amygdala was found active during trust behavior planning, while the basolateral amygdala was active during outcome evaluation. When planning trust behavior, central and basolateral amygdala activation differences between the players was related to whether participants learned to differentiate the players’ trustworthiness. Nucleus accumbens tracked whether trust was reciprocated but was not related to learning. This suggests learning whom to trust is not related to reward processing in the nucleus accumbens but rather to engagement of the basolateral amygdala. This study overcomes major empirical gaps between animal models and human neuroimaging and shows how different amygdala subnuclei and connected areas orchestrate learning to form different subjective trustworthiness beliefs about others and guide trust choice behavior.

Empathy for pain engages both shared affective responses and self-other distinction. In this study, we addressed the highly debated question of whether neural responses previously linked to affect sharing could result from the perception of salient affective displays. Moreover, we investigated how affect sharing and self-other distinction interact to determine our response to a pain that is either perceived as genuine or pretended (while in fact both were acted for reasons of experimental control). We found stronger activations in regions associated with affect sharing (anterior insula, aIns, and anterior mid-cingulate cortex, aMCC) as well as with affective self-other distinction (right supramarginal gyrus, rSMG), in participants watching video clips of genuine vs. pretended facial expressions of pain. Using dynamic causal modeling (DCM), we then assessed the neural dynamics between the right aIns and rSMG in these two conditions. This revealed a reduced inhibitory effect on the aIns to rSMG connection for genuine compared to pretended pain. For genuine pain only, brain-to-behavior regression analyses highlighted a linkage between this inhibitory effect on the one hand, and pain ratings as well as empathic traits on the other. These findings imply that if the pain of others is genuine and thus calls for an appropriate empathic response, neural responses in the aIns indeed seem related to affect sharing and self-other distinction is engaged to avoid empathic over-arousal. In contrast, if others merely pretend to be in pain, the perceptual salience of their painful expression results in neural responses that are down-regulated to avoid inappropriate affect sharing and social support.

Tobacco smoking is one of the leading causes of preventable death and disease worldwide. Most smokers want to quit, but relapse rates are high. To improve current smoking cessation treatments, a better understanding of the underlying mechanisms of nicotine dependence and related craving behaviour is needed. Studies on cue-driven cigarette craving have been a particularly useful tool for investigating the neural mechanisms of drug craving. Here, functional neuroimaging studies in humans have identified a core network of craving-related brain responses to smoking cues that comprises of amygdala, anterior cingulate cortex, orbitofrontal cortex, posterior cingulate cortex and ventral striatum. However, most functional Magnetic Resonance Imaging (fMRI) cue-reactivity studies do not adjust their stimuli for emotional valence, a factor assumed to confound craving-related brain responses to smoking cues. Here, we investigated the influence of emotional valence on key addiction brain areas by disentangling craving- and valence-related brain responses with parametric modulators in 32 smokers. For one of the suggested key regions for addiction, the amygdala, we observed significantly stronger brain responses to the valence aspect of the presented images than to the craving aspect. Our results emphasize the need for carefully selecting stimulus material for cue-reactivity paradigms, in particular with respect to emotional valence. Further, they can help designing future research on teasing apart the diverse psychological dimensions that comprise nicotine dependence and, therefore, can lead to a more precise mapping of craving-associated brain areas, an important step towards more tailored smoking cessation treatments.

Pain habituation is associated with a decrease of activation in brain areas related to pain perception. However, little is known about the specificity of these decreases to pain, as habituation has also been described for other responses like spinal reflexes and other sensory responses. Thus, it might be hypothesized that previously reported reductions in activation are not specifically related to pain habituation. For this reason, we performed a 3T fMRI study using either painful or non-painful electrical stimulation via an electrode attached to the back of the left hand. Contrasting painful vs. non-painful stimulation revealed significant activation clusters in regions well-known to be related to pain processing, such as bilateral anterior and posterior insula, primary/secondary sensory cortices (S1/S2) and anterior midcingulate cortex (aMCC). Importantly, our results show distinct habituation patterns for painful (in aMCC) and non-painful (contralateral claustrum) stimulation, while similar habituation for both types of stimulation was identified in bilateral inferior frontal gyrus (IFG) and contralateral S2. Our findings thus distinguish a general habituation in somatosensory processing (S2) and reduced attention (IFG) from specific pain and non-pain related habituation effects where pain-specific habituation effects within the aMCC highlight a change in affective pain perception.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments.With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

Tobacco smoking is one of the leading causes of preventable death and disease worldwide. Most smokers want to quit, but relapse rates are high. To improve current smoking cessation treatments, a better understanding of the underlying mechanisms of nicotine dependence and related craving behavior is needed. Studies on cue-driven cigarette craving have been a particularly useful tool for investigating the neural mechanisms of drug craving. Here, functional neuroimaging studies in humans have identified a core network of craving-related brain responses to smoking cues that comprises of amygdala, anterior cingulate cortex, orbitofrontal cortex, posterior cingulate cortex, and ventral striatum.However, most functional Magnetic Resonance Imaging (fMRI) cue-reactivity studies do not adjust their stimuli for emotional valence, a factor assumed to confound craving-driven brain responses to smoking cues. Here, we investigated the influence of emotional valence on key addiction brain areas by disentangling craving- and valence-related brain responses with parametric modulators in 32 smokers. For one of the suggested key regions for addiction, the amygdala, we observed significantly stronger brain responses to the valence aspect of the presented images than to the craving aspect. Our results emphasize the need for carefully selecting stimulus material for cue-reactivity paradigms, in particular with respect to emotional valence. Further, they can help designing future research on teasing apart the diverse psychological dimensions that comprise nicotine dependence, and, therefore, can lead to a more precise mapping of craving-associated brain areas, an important step towards more tailored smoking cessation treatments.

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs (i.e., self‐regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain‐based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning., Many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success, but the factors that cause this vast variability between participants remain unknown. Here, we used a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs could predict neurofeedback learning success. We were not able to identify common brain‐based success predictors across our diverse cohort of studies.