Effective connectivity reveals distinctive patterns in response to others' genuine affective experience of disgust.

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. In that prior study, effective connectivity between aIns and the right supramarginal gyrus (rSMG) was revealed to represent 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 (in fact the disgust expressions in both conditions were acted for reasons of experimental control). Consistent with the previous findings on pain, we found stronger activations in aIns associated with affect sharing for genuine disgust (inferred) 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 a few empathic traits. 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.

Disentangling craving- and valence-related brain responses to smoking cues in individuals with nicotine use disorder.

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.

Tailored haemodynamic response function increases detection power of fMRI in awake dogs (Canis familiaris).

Functional magnetic resonance imaging (fMRI) of awake and unrestrained dogs (Canis familiaris) has been established as a novel opportunity for comparative neuroimaging, promising important insights into the evolutionary roots of human brain function and cognition. However, data processing and analysis pipelines are often derivatives of methodological standards developed for human neuroimaging, which may be problematic due to profound neurophysiological and anatomical differences between humans and dogs. Here, we explore whether dog fMRI studies would benefit from a tailored dog haemodynamic response function (HRF). In two independent experiments, dogs were presented with different visual stimuli. BOLD signal changes in the visual cortex during these experiments were used for (a) the identification and estimation of a tailored dog HRF, and (b) the independent validation of the resulting dog HRF estimate. Time course analyses revealed that the BOLD signal in the primary visual cortex peaked significantly earlier in dogs compared to humans, while being comparable in shape. Deriving a tailored dog HRF significantly improved the model fit in both experiments, compared to the canonical HRF used in human fMRI. Using the dog HRF yielded significantly increased activation during visual stimulation, extending from the occipital lobe to the caudal parietal cortex, the bilateral temporal cortex, into bilateral hippocampal and thalamic regions. In sum, our findings provide robust evidence for an earlier onset of the dog HRF in two visual stimulation paradigms, and suggest that using such an HRF will be important to increase fMRI detection power in canine neuroimaging. By providing the parameters of the tailored dog HRF and related code, we encourage and enable other researchers to validate whether our findings generalize to other sensory modalities and experimental paradigms.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

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.

Targeting hippocampal hyperactivity with real-time fMRI neurofeedback: protocol of a single-blind randomized controlled trial in mild cognitive impairment.

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 ).

SmoCuDa: A Validated Smoking Cue Database to Reliably Induce Craving in Tobacco Use Disorder.

BACKGROUND: Cue-reactivity paradigms provide valuable insights into the underlying mechanisms of nicotine craving in nicotine-dependent subjects. In order to study cue-driven nicotine craving, robust and validated stimulus datasets are essential. OBJECTIVES: The aim of this study was to generate and validate a large set of individually rated smoking-related cues that allow for assessment of different stimulus intensities along the dimensions craving, valence, and arousal. METHODS: The image database consisted of 330 visual cues. Two hundred fifty smoking-associated pictures (Creative Commons license) were chosen from online databases and showed a widespread variety of smoking-associated content. Eighty pictures from previously published databases were included for cross-validation. Forty volunteers with tobacco use disorder rated "urge-to-smoke," "valence," and "arousal" for all images on a 100-point visual analogue scale. Pictures were also labelled according to 18 categories such as lit/unlit cigarettes in mouth, cigarette end, and cigarette in ashtray. RESULTS: Ratings (mean ± SD) were as follows: urge to smoke, 44.9 ± 13.2; valence, 51.2 ± 7.6; and arousal, 54.6 ± 7.1. All ratings, particularly "urge to smoke," were widely distributed along the whole scale spectrum. CONCLUSIONS: We present a novel image library of well-described smoking-related cues, which were rated on a continuous scale along the dimensions craving, valence, and arousal that accounts for inter-individual differences. The rating software, image database, and their ratings are publicly available at https://smocuda.github.io.

Reproducibility of amygdala activation in facial emotion processing at 7T.

Despite its importance as the prime method for non-invasive assessment of human brain function, functional MRI (fMRI) was repeatedly challenged with regards to the validity of the fMRI-derived brain activation maps. Amygdala fMRI was particularly targeted, as the amygdala's anatomical position in the ventral brain combined with strong magnetic field inhomogeneities and proximity to large vessels pose considerable obstacles for robust activation mapping. In this high-resolution study performed at ultra-high field (7T) fMRI, we aimed at (1) investigating systematic replicability of amygdala group-level activation in response to an established emotion processing task by varying task instruction and acquisition parameters and (2) testing for intra- and intersession reliability. At group-level, our results show statistically significant activation in bilateral amygdala and fusiform gyrus for each of the runs acquired. In addition, while fusiform gyrus activations are consistent across runs and sessions, amygdala activation levels show habituation effects across runs. This amygdala habituation effect is replicated in a session repeated two weeks later. Varying task instruction between matching emotions and matching persons does not change amygdala activation strength. Also, comparing two acquisition protocols with repetition times of either 700 â€‹ms or 1400 â€‹ms did not result in statistically significant differences of activation levels. Regarding within-subject reliability of amygdala activation, despite considerable variance in individual habituation patterns, we report fair to good inter-session reliability for the first run and excellent reliability for averages over runs. We conclude that high-resolution fMRI at 7T allows for robust mapping of amygdala activation in a broad range of variations. Our results of amygdala 7T fMRI are suitable to inform methodology and may encourage future studies to continue using emotion discrimination paradigms in clinical and non-clinical applications.

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

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.

Modulations in resting state networks of subcortical structures linked to creativity.

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.

Beware detrending: Optimal preprocessing pipeline for low-frequency fluctuation analysis.

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility to assess brain function independent of explicit tasks and individual performance. This absence of explicit stimuli in rs-fMRI makes analyses more susceptible to nonneural signal fluctuations than task-based fMRI. Data preprocessing is a critical procedure to minimise contamination by artefacts related to motion and physiology. We herein investigate the effects of different preprocessing strategies on the amplitude of low-frequency fluctuations (ALFFs) and its fractional counterpart, fractional ALFF (fALFF). Sixteen artefact reduction schemes based on nuisance regression are applied to data from 82 subjects acquired at 1.5 T, 30 subjects at 3 T, and 23 subjects at 7 T, respectively. In addition, we examine test-retest variance and effects of bias correction. In total, 569 data sets are included in this study. Our results show that full artefact reduction reduced test-retest variance by up to 50%. Polynomial detrending of rs-fMRI data has a positive effect on group-level t-values for ALFF but, importantly, a negative effect for fALFF. We show that the normalisation process intrinsic to fALFF calculation causes the observed reduction and introduce a novel measure for low-frequency fluctuations denoted as high-frequency ALFF (hfALFF). We demonstrate that hfALFF values are not affected by the negative detrending effects seen in fALFF data. Still, highest grey matter (GM) group-level t-values were obtained for fALFF data without detrending, even when compared to an exploratory detrending approach based on autocorrelation measures. From our results, we recommend the use of full nuisance regression including polynomial detrending in ALFF data, but to refrain from using polynomial detrending in fALFF data. Such optimised preprocessing increases GM group-level t-values by up to 60%.

Hippocampal Subfields in Acute and Remitted Depression-an Ultra-High Field Magnetic Resonance Imaging Study.

BACKGROUND: Studies investigating hippocampal volume changes after treatment with serotonergic antidepressants in patients with major depressive disorder yielded inconsistent results, and effects on hippocampal subfields are unclear. METHODS: To detail treatment effects on total hippocampal and subfield volumes, we conducted an open-label study with escitalopram followed by venlafaxine upon nonresponse in 20 unmedicated patients with major depressive disorder. Before and after 12 weeks treatment, we measured total hippocampal formation volumes and subfield volumes with ultra-high field (7 Tesla), T1-weighted, structural magnetic resonance imaging, and FreeSurfer. Twenty-eight remitted patients and 22 healthy subjects were included as controls. We hypothesized to detect increased volumes after treatment in major depressive disorder. RESULTS: We did not detect treatment-related changes of total hippocampal or subfield volumes in patients with major depressive disorder. Secondary results indicated that the control group of untreated, stable remitted patients, compared with healthy controls, had larger volumes of the right hippocampal-amygdaloid transition area and right fissure at both measurement time points. Depressed patients exhibited larger volumes of the right subiculum compared with healthy controls at MRI-2. Exploratory data analyses indicated lower baseline volumes in the subgroup of remitting (n = 10) vs nonremitting (n = 10) acute patients. CONCLUSIONS: The results demonstrate that monoaminergic antidepressant treatment in major depressive disorder patients was not associated with volume changes in hippocampal subfields. Studies with larger sample sizes to detect smaller effects as well as other imaging modalities are needed to further assess the impact of antidepressant treatment on hippocampal subfields.

Unsmoothed functional MRI of the human amygdala and bed nucleus of the stria terminalis during processing of emotional faces.

Functional neuroimaging of the human amygdala has been of great interest to uncover the neural underpinnings of emotions, mood, motivation, social cognition, and decision making, as well as their dysfunction in psychiatric disorders. Yet, several factors limit in vivo imaging of amygdalar function, most importantly its location deep within the temporal lobe adjacent to air-filled cavities that cause magnetic field inhomogeneities entailing signal dropouts. Additionally, the amygdala and the extended amygdalar region consist of several substructures, which have been assigned different functions and have important implications for functional and effective connectivity studies. Here we show that high-resolution ultra-high field fMRI at 7T can be used to overcome these fundamental challenges for acquisition and can meet some of the demands posed by the complex neuroanatomy and -physiology in this region. Utilizing the inherently high SNR, we use an optimized preprocessing and data analysis strategy to demonstrate that imaging of the (extended) amygdala is highly reliable and robust. Using unsmoothed single-subject data allowed us to differentiate brain activation during processing of emotional faces in the central and basolateral amygdala and, for the first time, in the bed nucleus of the stria terminalis (BNST), which is critically involved in the neural mechanisms of anxiety and threat monitoring. We also provide a quantitative assessment of single subject sensitivity, which is relevant for connectivity studies that rely on time course extraction of functionally-defined volumes of interest.

Ultra-high-field fMRI insights on insight: Neural correlates of the Aha!-moment.

Finding creative solutions to difficult problems is a fundamental aspect of human culture and a skill highly needed. However, the exact neural processes underlying creative problem solving remain unclear. Insightful problem solving tasks were shown to be a valid method for investigating one subcomponent of creativity: the Aha!-moment. Finding insightful solutions during a remote associates task (RAT) was found to elicit specific cortical activity changes. Considering the strong affective components of Aha!-moments, as manifested in the subjectively experienced feeling of relief following the sudden emergence of the solution of the problem without any conscious forewarning, we hypothesized the subcortical dopaminergic reward network to be critically engaged during Aha. To investigate those subcortical contributions to insight, we employed ultra-high-field 7 T fMRI during a German Version of the RAT. During this task, subjects were exposed to word triplets and instructed to find a solution word being associated with all the three given words. They were supposed to press a button as soon as they felt confident about their solution without further revision, allowing us to capture the exact event of Aha!-moment. Besides the finding on cortical involvement of the left anterior middle temporal gyrus (aMTG), here we showed for the first time robust subcortical activity changes related to insightful problem solving in the bilateral thalamus, hippocampus, and the dopaminergic midbrain comprising ventral tegmental area (VTA), nucleus accumbens (NAcc), and caudate nucleus. These results shed new light on the affective neural mechanisms underlying insightful problem solving.

OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis.

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.

Towards understanding rTMS mechanism of action: Stimulation of the DLPFC causes network-specific increase in functional connectivity.

Transcranial magnetic stimulation (TMS) is a powerful non-invasive technique for the modulation of brain activity. While the precise mechanism of action is still unknown, TMS is applied in cognitive neuroscience to establish causal relationships between stimulation and subsequent changes in cerebral function and behavioral outcome. In addition, TMS is an FDA-approved therapeutic agent in psychiatric disorders, especially major depression. Successful repetitive TMS in such disorders is usually applied over the left dorso-lateral prefrontal cortex (DLPFC) and treatment response mechanism was therefore supposed to be based on modulations in functional networks, particularly the meso-cortico-limbic reward circuit. However, mechanistic evidence for the direct effects of rTMS over DLPFC is sparse. Here we show the specificity and temporal evolution of rTMS effects by comparing connectivity changes within 20 common independent components in a sham-controlled study. Using an unbiased whole-brain resting-state network (RSN) approach, we successfully demonstrate that stimulation of left DLPFC modulates anterior cingulate cortex (ACC) connectivity in one specific meso-cortico-limbic network, while all other networks are neither influenced by rTMS nor by sham treatment. The results of this study show that the neural correlates of TMS treatment response are also traceable in DLPFC stimulation of healthy brains and therefore represent direct effects of the stimulation procedure.

Testosterone affects language areas of the adult human brain.

Although the sex steroid hormone testosterone is integrally involved in the development of language processing, ethical considerations mostly limit investigations to single hormone administrations. To circumvent this issue we assessed the influence of continuous high-dose hormone application in adult female-to-male transsexuals. Subjects underwent magnetic resonance imaging before and after 4 weeks of testosterone treatment, with each scan including structural, diffusion weighted and functional imaging. Voxel-based morphometry analysis showed decreased gray matter volume with increasing levels of bioavailable testosterone exclusively in Broca's and Wernicke's areas. Particularly, this may link known sex differences in language performance to the influence of testosterone on relevant brain regions. Using probabilistic tractography, we further observed that longitudinal changes in testosterone negatively predicted changes in mean diffusivity of the corresponding structural connection passing through the extreme capsule. Considering a related increase in myelin staining in rodents, this potentially reflects a strengthening of the fiber tract particularly involved in language comprehension. Finally, functional images at resting-state were evaluated, showing increased functional connectivity between the two brain regions with increasing testosterone levels. These findings suggest testosterone-dependent neuroplastic adaptations in adulthood within language-specific brain regions and connections. Importantly, deteriorations in gray matter volume seem to be compensated by enhancement of corresponding structural and functional connectivity. Hum Brain Mapp 37:1738-1748, 2016. © 2016 Wiley Periodicals, Inc.

Disrupted effective connectivity between the amygdala and orbitofrontal cortex in social anxiety disorder during emotion discrimination revealed by dynamic causal modeling for FMRI.

Social anxiety disorder (SAD) is characterized by over-reactivity of fear-related circuits in social or performance situations and associated with marked social impairment. We used dynamic causal modeling (DCM), a method to evaluate effective connectivity, to test our hypothesis that SAD patients would exhibit dysfunctions in the amygdala-prefrontal emotion regulation network. Thirteen unmedicated SAD patients and 13 matched healthy controls performed a series of facial emotion and object discrimination tasks while undergoing fMRI. The emotion-processing network was identified by a task-related contrast and motivated the selection of the right amygdala, OFC, and DLPFC for DCM analysis. Bayesian model averaging for DCM revealed abnormal connectivity between the OFC and the amygdala in SAD patients. In healthy controls, this network represents a negative feedback loop. In patients, however, positive connectivity from OFC to amygdala was observed, indicating an excitatory connection. As we did not observe a group difference of the modulatory influence of the FACE condition on the OFC to amygdala connection, we assume a context-independent reduction of prefrontal control over amygdalar activation in SAD patients. Using DCM, it was possible to highlight not only the neuronal dysfunction of isolated brain regions, but also the dysbalance of a distributed functional network.

(S)-citalopram influences amygdala modulation in healthy subjects: a randomized placebo-controlled double-blind fMRI study using dynamic causal modeling.

Citalopram and Escitalopram are gold standard pharmaceutical treatment options for affective, anxiety, and other psychiatric disorders. However, their neurophysiologic function on cortico-limbic circuits is incompletely characterized. Here we studied the neuropharmacological influence of Citalopram and Escitalopram on cortico-limbic regulatory processes by assessing the effective connectivity between orbitofrontal cortex (OFC) and amygdala using dynamic causal modeling (DCM) applied to functional MRI data. We investigated a cohort of 15 healthy subjects in a randomized, crossover, double-blind design after 10days of Escitalopram (10mg/d (S)-citalopram), Citalopram (10mg/d (S)-citalopram and 10mg/d (R)-citalopram), or placebo. Subjects performed an emotional face discrimination task, while undergoing functional magnetic resonance imaging (fMRI) scanning at 3 Tesla. As hypothesized, the OFC, in the context of the emotional face discrimination task, exhibited a down-regulatory effect on amygdala activation. This modulatory effect was significantly increased by (S)-citalopram, but not (R)-citalopram. For the first time, this study shows that (1) the differential effects of the two enantiomers (S)- and (R)-citalopram on cortico-limbic connections can be demonstrated by modeling effective connectivity methods, and (2) one of their mechanisms can be linked to an increased inhibition of amygdala activation by the orbitofrontal cortex.

Voxel-based morphometry at ultra-high fields. a comparison of 7T and 3T MRI data.

Recent technological progress enables MRI recordings at ultra-high fields of 7 T and above leading to brain images of higher resolution and increased signal-to-noise ratio. Despite these benefits, imaging at 7 T exhibits distinct challenges due to B1 field inhomogeneities, causing decreased image quality and problems in data analysis. Although several strategies have been proposed, a systematic investigation of bias-corrected 7 T data for voxel-based morphometry (VBM) is still missing and it is an ongoing matter of debate if VBM at 7 T can be carried out properly. Here, an optimized VBM study was conducted, evaluating the impact of field strength (3T vs. 7 T) and pulse sequence (MPRAGE vs. MP2RAGE) on gray matter volume (GMV) estimates. More specifically, twenty-two participants were measured under the conditions 3T MPRAGE, 7 T MPRAGE and 7 T MP2RAGE. Due to the fact that 7 T MPRAGE data exhibited strong intensity inhomogeneities, an alternative preprocessing pipeline was proposed and applied for that data. VBM analysis revealed higher GMV estimates for 7 T predominantly in superior cortical areas, caudate nucleus, cingulate cortex and the hippocampus. On the other hand, 3T yielded higher estimates especially in inferior cortical areas of the brain, cerebellum, thalamus and putamen compared to 7 T. Besides minor exceptions, these results were observed for 7 T MPRAGE as well for the 7 T MP2RAGE measurements. Results gained in the inferior parts of the brain should be taken with caution, as native GM segmentations displayed misclassifications in these regions for both 7 T sequences. This was supported by the test-retest measurements showing highest variability in these inferior regions of the brain for 7 T and also for the advanced MP2RAGE sequence. Hence, our data support the use of 7 T MRI for VBM analysis in cortical areas, but direct comparison between field strengths and sequences requires careful assessment. Similarly, analysis of the inferior cortical regions, cerebellum and subcortical regions still remains challenging at 7 T even if the advanced MP2RAGE sequence is used.

Comparison of continuously acquired resting state and extracted analogues from active tasks.

Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting-state data, the application to task-specific fMRI has received growing attention. Three major methods for extraction of resting-state data from task-related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in-between task blocks. Despite widespread application in current research, consensus on which method best resembles resting-state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting-state, two different task paradigms were assessed (emotion discrimination and right finger-tapping) and five well-described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting-state (Dice, Intraclass correlation coefficient (ICC), R(2) ) showed that regression against task effects yields functional connectivity networks most alike to resting-state. However, all methods exhibited significant differences when compared to continuous resting-state and similarity metrics were lower than test-retest of two resting-state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting-state when extracting signals from task designs, although functional connectivity computed from task-specific data may indeed yield interesting information.