Effects of Adult Age and Functioning of the Locus Coeruleus Norepinephrinergic System on Reward-Based Learning.

Age-related impairments in value representations and updating during decision-making and reward-based learning are often related to age-related attenuation in the catecholamine system such as dopamine (DA) and norepinephrine (NE). However, it is unclear to what extent age-related declines in NE functioning in humans affect reward-based decision-making. We conducted a probabilistic decision-making task and applied a Q-learning model to investigate participants' anticipatory values and value sensitivities. Task-related pupil dilations and locus coeruleus (LC) magnetic resonance imaging (MRI) contrast, which served as a potential window of the LC-NE functions, were assessed in younger and older adults. Results showed that in both choice and feedback phases, younger adults' (N = 42, 22 males) pupil dilations negatively correlated with anticipatory values, indicating uncertainty about outcome probabilities. Uncertainty-evoked pupil dilations in older adults (N = 41, 27 males) were smaller, indicating age-related impairments in value estimation and updating. In both age groups, participants who showed a larger uncertainty-evoked pupil dilation exhibited a higher value sensitivity as reflected in the ß parameter of the reinforcement Q-learning model. Furthermore, older adults (N = 34, 29 males) showed a lower LC-MRI contrast than younger adults (N = 25, 15 males). The LC-MRI contrast positively correlated with value sensitivity only in older but not in younger adults. These findings suggest that task-related pupillary responses can reflect age-related deficits in value estimation and updating during reward-based decision-making. Our evidence with the LC-MRI contrast further showed the age-related decline of the LC structure in modulating value representations during reward-based learning.SIGNIFICANCE STATEMENT Age-related impairments in value representation and updating during reward-based learning are associated with declines in the catecholamine modulation with age. However, it is unclear how age-related declines in the LC-NE system may affect reward-based learning. Here, we show that compared with younger adults, older adults exhibited reduced uncertainty-induced pupil dilations, suggesting age-related deficits in value estimation and updating. Older adults showed a lower structural MRI of the LC contrast than younger adults, indicating age-related degeneration of the LC structure. The association between the LC-MRI contrast and value sensitivity was only observed in older adults. Our findings may demonstrate a pioneering model to unravel the role of the LC-NE system in reward-based learning in aging.

Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features.

BACKGROUND: Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features. METHODS: Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar). RESULTS: For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11-0.361) and a balanced accuracy of 63.1% (95% CI 55.9-70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI -0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6-67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance. CONCLUSIONS: Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

Observing cognitive processes in time through functional MRI model comparison.

The temporal specificity of functional magnetic resonance imaging (fMRI) is limited by a sluggish and locally variable hemodynamic response trailing the neural activity by seconds. Here, we demonstrate for an attention capture paradigm that it is, never the less, possible to extract information about the relative timing of regional brain activity during cognitive processes on the scale of 100 ms by comparing alternative signal models representing early versus late activation. We demonstrate that model selection is not driven by confounding regional differences in hemodynamic delay. We show, including replication, that the activity in the dorsal anterior insula is an early signal predictive of behavioral performance, while amygdala and ventral anterior insula signals are not. This specific finding provides new insights into how the brain assigns salience to stimuli and emphasizes the role of the dorsal anterior insula in this context. The general analytic approach, named "Cognitive Timing through Model Comparison" (CTMC), offers an exciting and novel method to identify functional brain subunits and their causal interactions.

Aberrant functional brain network organization is associated with relapse during 1-year follow-up in alcohol-dependent patients.

Alcohol dependence (AD) is a debilitating disease associated with high relapse rates even after long periods of abstinence. Thus, elucidating neurobiological substrates of relapse risk is fundamental for the development of novel targeted interventions that could promote long-lasting abstinence. In the present study, we analysed resting-state functional magnetic resonance imaging (rsfMRI) data from a sample of recently detoxified patients with AD (n = 93) who were followed up for 12 months after rsfMRI assessment. Specifically, we employed graph theoretic analyses to compare functional brain network topology and functional connectivity between future relapsers (REL, n = 59), future abstainers (ABS, n = 28) and age- and gender-matched controls (CON, n = 83). Our results suggest increased whole-brain network segregation, decreased global network integration and overall blunted connectivity strength in REL compared with CON. Conversely, we found evidence for a comparable network architecture in ABS relative to CON. At the nodal level, REL exhibited decreased integration and decoupling between multiple brain systems compared with CON, encompassing regions associated with higher-order executive functions, sensory and reward processing. Among patients with AD, increased coupling between nodes implicated in reward valuation and salience attribution constitutes a particular risk factor for future relapse. Importantly, aberrant network organization in REL was consistently associated with shorter abstinence duration during follow-up, portending to a putative neural signature of relapse risk in AD. Future research should further evaluate the potential diagnostic value of the identified changes in network topology and functional connectivity for relapse prediction at the individual subject level.

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.

Questioning the role of amygdala and insula in an attentional capture by emotional stimuli task.

Our senses are constantly monitoring the environment for emotionally salient stimuli that are potentially relevant for survival. Because of our limited cognitive resources, emotionally salient distractors prolong reaction times (RTs) as compared to neutral distractors. In addition, many studies have reported fMRI blood oxygen level-dependent (BOLD) activation of both the amygdala and the anterior insula for similar valence contrasts. However, a direct correlation of trail-by-trial BOLD activity with RTs has not been shown, yet, which would be a crucial piece of evidence to relate the two observations. To investigate the role of the above two regions in the context of emotional distractor effects, we study here the correlation between BOLD activity and RTs for a simple attentional capture by emotional stimuli (ACES) choice reaction time task using a general linear subject-level model with a parametric RT regressor. We found significant regression coefficients in the anterior insula, supplementary motor cortex, medial precentral regions, sensory-motor areas and others, but not in the amygdala, despite activation of both insula and amygdala in the traditional valence contrast across trials (i.e., negative vs. neutral pictures). In addition, we found that subjects that exhibit a stronger RT distractor effect across trials also show a stronger BOLD valence contrast in the right anterior insula but not in the amygdala. Our results indicate that the current neuroimaging-based evidence for the involvement of the amygdala in RT slowing is limited. We advocate that models of emotional capture should incorporate both the amygdala and the anterior insula as separate entities.

Effects of moderate alcohol levels on default mode network connectivity in heavy drinkers.

BACKGROUND: It is well established that even moderate levels of alcohol affect cognitive functions such as memory, self-related information processing, and response inhibition. Nevertheless, the neural mechanisms underlying these alcohol-induced changes are still unclear, especially on the network level. The default mode network (DMN) plays an important role in memory and self-initiated mental activities; hence, studying functional interactions of the DMN may provide new insights into the neural mechanisms underlying alcohol-related changes. METHODS: We investigated resting-state functional connectivity (rsFC) of the DMN in a cohort of 37 heavy drinkers at a breath alcohol concentration of 0.8 g/kg. Alcohol and saline were infused in a single-blind crossover design. RESULTS: Intranetwork connectivity analyses revealed that participants showed significantly decreased rsFC of the right hippocampus and right middle temporal gyrus during acute alcohol exposure. Moreover, follow-up analyses revealed that these rsFC decreases were more pronounced in participants who reported stronger craving for alcohol. Exploratory internetwork connectivity analyses of the DMN with other resting-state networks showed no significant alcohol-induced changes, but suffered from low statistical power. CONCLUSIONS: Our results indicate that acute alcohol exposure affects rsFC within the DMN. Functionally, this finding may be associated with impairments in memory encoding and self-referential processes commonly observed during alcohol intoxication. Future resting-state functional magnetic resonance imaging studies might therefore also investigate memory function and test whether DMN-related connectivity changes are associated with alcohol-induced impairments or craving.

A comparison of fMRI and behavioral models for predicting inter-temporal choices.

In an inter-temporal choice (IteCh) task, subjects are offered a smaller amount of money immediately or a larger amount at a later time point. Here, we are using trial-by-trial fMRI data from 363 recording sessions and machine learning in an attempt to build a classifier that would ideally outperform established behavioral model given that it has access to brain activity specific to a single trial. Such methods could allow for future investigations of state-like factors that influence IteCh choices. To investigate this, coefficients of a GLM with one regressor per trial were used as features for a support vector machine (SVM) in combination with a searchlight approach for feature selection and cross-validation. We then compare the results to the performance of four different behavioral models. We found that the behavioral models reached mean accuracies of 90% and above, while the fMRI model only reached 54.84% at the best location in the brain with a spatial distribution similar to the well-known value-tracking network. This low, though significant, accuracy is in line with simulations showing that classifying based on signals with realistic correlations with subjective value produces comparable, low accuracies. These results emphasize the limitations of fMRI recordings from single events to predict human choices, especially when compared to conventional behavioral models. Better performance may be obtained with paradigms that allow the construction of miniblocks to improve the available signal-to-noise ratio.

Altered global brain network topology as a trait marker in patients with anorexia nervosa.

BACKGROUND: Resting state functional magnetic resonance imaging studies have identified functional connectivity patterns associated with acute undernutrition in anorexia nervosa (AN), but few have investigated recovered patients. Thus, a trait connectivity profile characteristic of the disorder remains elusive. Using state-of-the-art graph-theoretic methods in acute AN, the authors previously found abnormal global brain network architecture, possibly driven by local network alterations. To disentangle trait from starvation effects, the present study examines network organization in recovered patients. METHODS: Graph-theoretic metrics were used to assess resting-state network properties in a large sample of female patients recovered from AN (recAN, n = 55) compared with pairwise age-matched healthy controls (HC, n = 55). RESULTS: Indicative of an altered global network structure, recAN showed increased assortativity and reduced global clustering as well as small-worldness compared with HC, while no group differences at an intermediate or local network level were evident. However, using support-vector classifier on local metrics, recAN and HC could be separated with an accuracy of 70.4%. CONCLUSIONS: This pattern of results suggests that long-term recovered patients have an aberrant global brain network configuration, similar to acutely underweight patients. While the finding of increased assortativity may represent a trait marker of AN, the remaining findings could be seen as a scar following prolonged undernutrition.

Addiction Research Consortium: Losing and regaining control over drug intake (ReCoDe)-From trajectories to mechanisms and interventions.

One of the major risk factors for global death and disability is alcohol, tobacco, and illicit drug use. While there is increasing knowledge with respect to individual factors promoting the initiation and maintenance of substance use disorders (SUDs), disease trajectories involved in losing and regaining control over drug intake (ReCoDe) are still not well described. Our newly formed German Collaborative Research Centre (CRC) on ReCoDe has an interdisciplinary approach funded by the German Research Foundation (DFG) with a 12-year perspective. The main goals of our research consortium are (i) to identify triggers and modifying factors that longitudinally modulate the trajectories of losing and regaining control over drug consumption in real life, (ii) to study underlying behavioral, cognitive, and neurobiological mechanisms, and (iii) to implicate mechanism-based interventions. These goals will be achieved by: (i) using mobile health (m-health) tools to longitudinally monitor the effects of triggers (drug cues, stressors, and priming doses) and modify factors (eg, age, gender, physical activity, and cognitive control) on drug consumption patterns in real-life conditions and in animal models of addiction; (ii) the identification and computational modeling of key mechanisms mediating the effects of such triggers and modifying factors on goal-directed, habitual, and compulsive aspects of behavior from human studies and animal models; and (iii) developing and testing interventions that specifically target the underlying mechanisms for regaining control over drug intake.

Modulating functional connectivity between medial frontopolar cortex and amygdala by inhibitory and excitatory transcranial magnetic stimulation.

The prefrontal-limbic network in the human brain plays a major role in social cognition, especially cognitive control of emotion. The medial frontopolar cortex (mFP; Brodmann Area 10) and the amygdala are part of this network and display correlated neuronal activity in time, as measured by functional magnetic resonance imaging (fMRI). This functional connectivity is dynamic, sensitive to training, and affected in mental disorders. However, the effects of neurostimulation on functional connectivity within this network have not yet been systematically investigated. Here, we investigate the effects of both low- and high-frequency repetitive transcranial magnetic stimulation (rTMS) to the right mFP on functional connectivity between mFP and amygdala, as measured with resting state fMRI (rsfMRI). Three groups of healthy participants received either low-frequency rTMS (1 Hz; N = 18), sham TMS (1 Hz, subthreshold; N = 18) or high-frequency rTMS (20 Hz; N = 19). rsfMRI was acquired before and after (separate days). We hypothesized a modulation of functional connectivity in opposite directions compared to sham TMS through adjustment of the stimulation frequency. Groups differed in functional connectivity between mFP and amygdala after stimulation compared to before stimulation (low-frequency: decrease, high-frequency: increase). Motion or induced changes in neuronal activity were excluded as confounders. Results show that rTMS is effective for increasing and decreasing functional coherence between prefrontal and limbic regions. This finding is relevant for social and affective neuroscience as well as novel treatment approaches in psychiatry.

Acute tryptophan loading decreases functional connectivity between the default mode network and emotion-related brain regions.

It has been shown that the functional architecture of the default mode network (DMN) can be affected by serotonergic challenges and these effects may provide insights on the neurobiological bases of depressive symptomatology. To deepen our understanding of this possible interplay, we used a double-blind, randomized, cross-over design, with a control condition and two interventions to decrease (tryptophan depletion) and increase (tryptophan loading) brain serotonin synthesis. Resting-state fMRI from 85 healthy subjects was acquired for all conditions 3 hr after the ingestion of an amino acid mixture containing different amounts of tryptophan, the dietary precursor of serotonin. The DMN was derived for each participant and session. Permutation testing was performed to detect connectivity changes within the DMN as well as between the DMN and other brain regions elicited by the interventions. We found that tryptophan loading increased tryptophan plasma levels and decreased DMN connectivity with visual cortices and several brain regions involved in emotion and affect regulation (i.e., putamen, subcallosal cortex, thalamus, and frontal cortex). Tryptophan depletion significantly reduced tryptophan levels but did not affect brain connectivity. Subjective ratings of mood, anxiety, sleepiness, and impulsive choice were not strongly affected by any intervention. Our data indicate that connectivity between the DMN and emotion-related brain regions might be modulated by changes in the serotonergic system. These results suggest that functional changes in the brain associated with different brain serotonin levels may be relevant to understand the neural bases of depressive symptoms.

Dynamic changes in white matter microstructure in anorexia nervosa: findings from a longitudinal study.

BACKGROUND: Gray matter (GM) 'pseudoatrophy' is well-documented in patients with anorexia nervosa (AN), but changes in white matter (WM) are less well understood. Here we investigated the dynamics of microstructural WM brain changes in AN patients during short-term weight restoration in a combined longitudinal and cross-sectional study design. METHODS: Diffusion-weighted images were acquired in young AN patients before (acAN-Tp1, n = 56) and after (acAN-Tp2, n = 44) short-term weight restoration as well as in age-matched healthy controls (HC, n = 60). Images were processed using Tract-Based-Spatial-Statistics to compare fractional anisotropy (FA) across groups and timepoints. RESULTS: In the cross-sectional comparison, FA was significantly reduced in the callosal body in acAN-Tp1 compared with HC, while no differences were found between acAN-Tp2 and HC. In the longitudinal arm, FA increased with weight gain in acAN-Tp2 relative to acAN-Tp1 in large parts of the callosal body and the fornix, while it decreased in the right corticospinal tract. CONCLUSIONS: Our findings reveal that dynamic, bidirectional changes in WM microstructure in young underweight patients with AN can be reversed with brief weight restoration therapy. These results parallel those previously observed in GM and suggest that alterations in WM in non-chronic AN are also state-dependent and rapidly reversible with successful intervention.

Elevated cognitive control over reward processing in recovered female patients with anorexia nervosa.

BACKGROUND: Individuals with anorexia nervosa are thought to exert excessive self-control to inhibit primary drives. METHODS: This study used functional MRI (fMRI) to interrogate interactions between the neural correlates of cognitive control and motivational processes in the brain reward system during the anticipation of monetary reward and reward-related feedback. In order to avoid confounding effects of undernutrition, we studied female participants recovered from anorexia nervosa and closely matched healthy female controls. The fMRI analysis (including node-to-node functional connectivity) followed a region of interest approach based on models of the brain reward system and cognitive control regions implicated in anorexia nervosa: the ventral striatum, medial orbitofrontal cortex (mOFC) and dorsolateral prefrontal cortex (DLPFC). RESULTS: We included 30 recovered patients and 30 controls in our study. There were no behavioural differences and no differences in hemodynamic responses of the ventral striatum and the mOFC in the 2 phases of the task. However, relative to controls, recovered patients showed elevated DLPFC activity during the anticipation phase, failed to deactivate this region during the feedback phase and displayed greater functional coupling between the DLPFC and mOFC. Recovered patients also had stronger associations than controls between anticipation-related DLPFC responses and instrumental responding. LIMITATIONS: The results we obtained using monetary stimuli might not generalize to other forms of reward. CONCLUSION: Unaltered neural responses in ventral limbic reward networks but increased recruitment of and connectivity with lateral-frontal brain circuitry in recovered patients suggests an elevated degree of selfregulatory processes in response to rewarding stimuli. An imbalance between brain systems subserving bottom-up and top-down processes may be a trait marker of the disorder.

Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes.

The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve upon the early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using a linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2-74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0-71.9) for leave-one-site-out. Structural alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.

Altered White Matter Connectivity in Young Acutely Underweight Patients With Anorexia Nervosa.

OBJECTIVE: Reductions of gray matter volume and cortical thickness in anorexia nervosa (AN) are well documented. However, findings regarding the integrity of white matter (WM) as studied via diffusion weighted imaging (DWI) are remarkably heterogeneous, and WM connectivity has been examined only in small samples using a limited number of regions of interest. The present study investigated whole-brain WM connectivity for the first time in a large sample of acutely underweight patients with AN. METHOD: DWI data from predominantly adolescent patients with acute AN (n = 96, mean age = 16.3 years) and age-matched healthy control participants (n = 96, mean age = 17.2 years) were analyzed. WM connectivity networks were generated from fiber-tractography-derived streamlines connecting 233 cortical/subcortical regions. To identify group differences, network-based statistic was used while taking head motion, WM, and ventricular volume into account. RESULTS: Patients with AN were characterized by 6 WM subnetworks with abnormal architecture, as indicated by increased fractional anisotropy located primarily in parietal-occipital regions and accompanied by reduced radial diffusivity. Group differences based on number of streamlines reached only nominal significance. CONCLUSION: Our study reveals pronounced alterations in the WM connectome in young patients with AN. In contrast to known reductions in gray matter in the acutely underweight state of AN, this pattern does not necessarily indicate a deterioration of the WM network. Future studies using advanced MRI sequences will have to clarify interrelations with axonal packing or myelination, and whether the changes should be considered a consequence of undernutrition or a vulnerability for developing or maintaining AN.

Individuals at increased risk for development of bipolar disorder display structural alterations similar to people with manifest disease.

In psychiatry, there has been a growing focus on identifying at-risk populations. For schizophrenia, these efforts have led to the development of early recognition and intervention measures. Despite a similar disease burden, the populations at risk of bipolar disorder have not been sufficiently characterized. Within the BipoLife consortium, we used magnetic resonance imaging (MRI) data from a multicenter study to assess structural gray matter alterations in N = 263 help-seeking individuals from seven study sites. We defined the risk using the EPIbipolar assessment tool as no-risk, low-risk, and high-risk and used a region-of-interest approach (ROI) based on the results of two large-scale multicenter studies of bipolar disorder by the ENIGMA working group. We detected significant differences in the thickness of the left pars opercularis (Cohen's d = 0.47, p = 0.024) between groups. The cortex was significantly thinner in high-risk individuals compared to those in the no-risk group (p = 0.011). We detected no differences in the hippocampal volume. Exploratory analyses revealed no significant differences in other cortical or subcortical regions. The thinner cortex in help-seeking individuals at risk of bipolar disorder is in line with previous findings in patients with the established disorder and corresponds to the region of the highest effect size in the ENIGMA study of cortical alterations. Structural alterations in prefrontal cortex might be a trait marker of bipolar risk. This is the largest structural MRI study of help-seeking individuals at increased risk of bipolar disorder.

PyParadigm-A Python Library to Build Screens in a Declarative Way.

In experimental psychology, subjects are often confronted with computer-based experimental paradigms. Creating such paradigms can require a lot of effort. PyParadigm is a newly developed Python library to ease the development of such paradigms by employing a declarative approach to build user interfaces (UIs). Paradigm specifications in this approach requires much less code and training than in alternative libraries. Although PyParadigm was initially developed for the creation of experimental paradigms, it is generally suited to build UIs that display or interact with 2D objects.

No evidence for the involvement of serotonin or the 5-HTTLPR genotype in intertemporal choice in a larger community sample.

BACKGROUND: Serotonin has been implicated in impulsive behaviours such as temporal discounting. While animal studies and theoretical approaches suggest that reduced tonic serotonin levels increase temporal discounting rates and vice versa, evidence from human studies is scarce and inconclusive. Furthermore, an important modulator of serotonin signalling, a genetic variation in the promoter region of the serotonin transporter gene (5-HTTLPR), has not been investigated for temporal discounting so far. OBJECTIVE: First, the purpose of this study was to test for a significant association between 5-HTTLPR and temporal discounting. Second, we wished to investigate the effect of high/low tonic serotonin levels on intertemporal choice and blood oxygen-level-dependent response, controlling for 5-HTTLPR. METHODS: We tested the association of 5-HTTLPR with temporal discounting rates using an intertemporal choice task in 611 individuals. We then manipulated tonic serotonin levels with acute tryptophan interventions (depletion, loading, balanced) in a subsample of 45 short (S)-allele and 45 long (L)/L-allele carriers in a randomised double-blind crossover design using functional magnetic resonance imaging and an intertemporal choice task. RESULTS: Overall, we did not find any effect of serotonin and 5-HTTLPR on temporal discounting rates or the brain networks associated with valuation and cognitive control. CONCLUSION: Our findings indicate that serotonin may not be directly involved in choices including delays on longer timescales such as days, weeks or months. We speculate that serotonin plays a stronger role in dynamic intertemporal choice tasks where the delays are on a timescale of seconds and hence are therefore directly experienced during the experiment.