Biology subjects

Kirschner, M.

Publications and source records attributed to Kirschner, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Longitudinal White Matter Microstructure and Transition to Psychosis in Individuals at Clinical High Risk

Altered white matter microstructure, commonly indexed by fractional anisotropy (FA), has been reported in individuals at clinical high-risk of psychosis (CHR-P) across multiple white matter tracts. However, the relationship between these alterations and later transition to psychosis remains unclear. To address this gap, we investigated whether baseline and longitudinally updated FA measurements were associated with risk of transition to psychosis in a large cohort of individuals at CHR-P. We analysed diffusion tensor imaging data from N = 205 participants at CHR-P, 23 of whom developed psychosis within 3 years of follow-up. Data were collected at baseline, 6 months, and 12 months. Associations between global and tract-specific FA and transition risk were examined using Cox proportional hazards models of baseline data and longitudinally updated data. Point estimates were directionally consistent, with hazard ratios <1 for FA across all white matter tracts in both baseline and longitudinal models. Associations reached nominal significance in the hippocampal cingulum, anterior thalamic radiation, and forceps minor, but none of these remained significant after correcting for multiple comparisons (pcorr > .05). The directionally consistent estimates are compatible with small, distributed effects across multiple white matter pathways being associated with transition to psychosis, although this requires replication. Longitudinally updated FA measurements did not yield stronger associations than were available from baseline imaging data alone. Studies at an even larger scale are needed to establish whether these associations are robust and clinically meaningful.

neuroscience↗

bioRxiv2026

The Trajectory Statistics of Biological Exploratory Dynamics

Biological processes, from molecules diffusing to their regulatory destinations to whales pursuing food or mates, are widely exploratory. Such behaviors are effective when some verifiable functional state or outcome can be reached regardless of initial conditions. In these cases, systems repeatedly undergo distinct and abortive trajectories; the process only ends when the system finds 'right' outcomes. This dominance of the terminal condition (and indifference to the initial state) provokes a very different perspective than the conventional 'dynamical systems' framework that has been a centerpiece of the quantitative sciences for centuries. We hypothesize that many of these problems defy the initial-condition driven or gradients on landscapes so useful in problems ranging from mechanics to electrodynamics to chemical kinetics to mass and heat transport. We examine several mathematical frameworks that capture and unify key aspects of exploratory dynamics. One powerful way of thinking of such processes is the geometric distribution, where repeated failures are punctuated by a successful trajectory. We develop intuitions for how random walks with resets accelerate search processes. We highlight fresh and surprising behaviors of search under drift, cues, and checkpoints. Last, appreciating the probability of trajectories conditioned on satisfying macroscopic final outcomes reveals a language for the potency of variation sculpted by selection in their broadest forms. This can give the fictitious appearance of the future making itself known in the present, but we view this as conceptually similar to the way 'fictitious forces' arise in non-inertial reference frames. These approaches stress unity, open questions, and applications across a range of biological phenomena.

biophysics↗

bioRxiv2026

HippoGenes: A robust workflow for subregional transcriptomic profiling of the human hippocampus

The human hippocampus is a unique cortical structure central to brain function, plasticity, and disease. Unravelling its complex organization requires the integration of multiscale data, linking molecular features to mesoscale anatomy and macroscale functional patterns. Gene expression is a fundamental microscale phenotype, and its profiling can provide a reference description of how molecular features are distributed across the brain. Capitalizing on recent imaging-transcriptomic analyses, we introduce HippoGenes, a repository of fine-grained gene expression patterns across human hippocampal subregions. We leveraged spatial statistical models and hippocampal surface mapping to reconstruct dense transcriptomic maps from sparse post-mortem tissue samples of the Allen Human Brain Atlas, generating continuous expression estimates for thousands of genes aligned to a common surface-based coordinate system. We illustrate the utility of HippoGenes to (i) map medial-lateral and anterior-posterior transcriptomic gradients that align with subfield and tripartite subdivisions of the hippocampal formation, (ii) examine associations between gene expression and canonical microstructural and functional features of the hippocampus, and (iii) perform a molecular decoding of subregional alterations in neurological patients with hippocampal pathology. HippoGenes provides a framework for exploring the molecular organization of the hippocampus, opening avenues for multiscale integration in health and disease, and is openly available on https://hippogenes.readthedocs.io/.

neuroscience↗

bioRxiv2026

Longitudinal White Matter Trajectories in Clinical High Risk and First-Episode Psychosis: Findings from the Multi-Centre PSYSCAN Study

Psychosis has been linked to changes in diffusion-derived fractional anisotropy (FA) across multiple white matter tracts, yet most evidence is cross-sectional. Longitudinal findings are inconsistent, and the clinical relevance of white matter changes over time remains unclear. To address this gap, we investigated longitudinal white matter changes and their clinical correlates in early psychosis in a large, multi-centre diffusion-tensor imaging study. Across 18 sites, 407 participants (healthy controls: n = 97, 59.8% men, mean age 23.9{+/-}4.6 years; clinical high risk of psychosis: n = 158, 53.5% men, mean age 23.0{+/-}4.9 years; first-episode psychosis: n = 152, 71.1% men, mean age 25.1{+/-}5.4 years) were scanned at up to three time points over 12 months. FA was assessed in the cingulum bundle, the superior longitudinal fasciculus, the inferior fronto-occipital fasciculus, and at the whole brain level. FA trajectories were analysed using linear mixed-effects models to test effects of group, social and occupational functioning, and (attenuated) psychotic symptoms, while controlling for demographic and socioeconomic covariates. No significant group differences in FA were observed, either globally or within tracts (p > .05). Longitudinal FA trajectories were not associated with changes in (attenuated) psychotic symptoms or functioning (p > .05). In contrast, higher baseline antipsychotic medication dose was significantly associated with lower FA (pcorr < .05). Overall, these findings indicate that white matter microstructure is relatively stable during the clinical-high-risk and first-episode phases of psychosis, and that it is not clearly associated with variation in symptom severity or functional outcomes over time.

neuroscience↗

bioRxiv2025

Kidney Tissue Characterization using Normalized Raman Imaging and Segment-Anything

Normalized Raman Imaging (NoRI) enables high-resolution, label-free quantification of protein and lipid concentrations in biological tissues. Because NoRI provides rich molecular information, the analysis of its large, multi-channel datasets turns into a significant computational bottleneck. In this work, we introduce a novel, modular computational pipeline for automated segmentation and quantification of kidney tissue structures imaged with NoRI. The pipeline integrates classical image processing with state-of-the-art machine learning tools, including the Segment Anything Model (SAM) and ilastik, to segment key anatomical and biochemical features--such as tubules, nuclei, brush borders, and lumens. A custom contrast-enhancement strategy was developed to create a third SAM input channel from NoRI data, leading to a substantial improvement in segmentation performance (F1 score: 0.9226). Our framework enables accurate cytoplasm, resolved quantification of protein and lipid concentrations and reveals distinct biochemical signatures across renal tubule subtypes and experimental conditions. This method offers a robust, scalable foundation for quantitative tissue analysis and enhances the utility of NoRI imaging for biomedical research.

bioinformatics↗

bioRxiv2024

Bridging Glucose Metabolism and Intrinsic Functional Organization of the Human Cortex

The human brain requires a continuous supply of energy to function effectively. Here, we investigated how the low-dimensional organization of intrinsic functional connectivity patterns based on resting-state functional magnetic resonance imaging relates to brain energy expenditure measured by fluorodeoxyglucose positron emission tomography. By incrementally adding more dimensions of brain organization (via functional gradients), we were able to show that increasing amounts of variance in the map of brain energy expenditure could be accounted for. In particular, the brain organization dimensions that explained a large amount of the variance in intrinsic brain function also explained a large amount of the regional variance in the energy expenditure maps. This was particularly true for brain organization maps based on the strongest connections, suggesting that "weak" connections may not explain as much energy variance. Notably, our topological model was more effective than random brain organization configurations, suggesting that brain organization may be specifically associated with energy optimization. Finally, using brain asymmetry as a model for metabolic efficiency, we found that optimizing energy expenditure independently in each hemisphere outperformed non-independent optimization. This supports the concept of hemispheric competition rather than lateralization in energy allocation. Our results demonstrate how the spatial organization of functional connections is systematically linked to optimized energy expenditure in the human brain, providing new insights into the metabolic basis of brain function.

neuroscience↗

bioRxiv2024

Neurodevelopmentally rooted epicenters in schizophrenia: sensorimotor-association spatial axis of cortical thickness alterations

Pathologic perturbations in schizophrenia have been suggested to propagate via the functional and structural connectome across the lifespan. Yet how the connectome guides early cortical reorganization of developing schizophrenia remains unknown. Here, we used early-onset schizophrenia (EOS) as a neurodevelopmental disease model to investigate putative early pathologic origins that propagate through the functional and structural connectome. We compared 95 patients with antipsychotic-naive first-episode EOS and 99 typically developing controls (7-17 years of age, 120 females). Whereas patients showed widespread cortical thickness reductions, thickness increases were observed in primary cortical areas. Using normative connectomics models, we found that epicenters of thickness reductions were situated in association regions linked to language, affective, and cognitive functions, while epicenters of increased thickness in EOS were located in sensorimotor regions subserving visual, somatosensory, and motor functions. Using post-mortem transcriptomic data of six donors, we observed that the epicenter map differentiated oligodendrocyte-related transcriptional changes at its sensory apex and the association end was related to expression of excitatory/inhibitory neurons. More generally, we observed that the epicenter map was associated with neurodevelopmental disease gene dysregulation and human accelerated region genes, suggesting potential shared genetic determinants across various neurodevelopmental disorders. Taken together, our results underscore the developmentally rooted pathologic origins of schizophrenia and their transcriptomic overlap with other neurodevelopmental diseases.

neuroscience↗

bioRxiv2024

HippoMaps: Multiscale cartography of the human hippocampal formation

The hippocampus has a unique microarchitecture, is situated at the nexus of multiple macroscale functional networks, contributes to numerous cognitive as well as affective processes, and is highly susceptible to brain pathology across common disorders. These features make the hippocampus a model to understand how brain structure covaries with function, in both health and disease. Here, we introduce HippoMaps, an open access toolbox and online data warehouse for the mapping and contextualization of subregional hippocampal data in the human brain (http://hippomaps.readthedocs.io). HippoMaps capitalizes on a unified hippocampal unfolding approach as well as shape intrinsic registration capabilities to allow for cross-subject and cross-modal data aggregation. We initialize this repository with an unprecedented combination of hippocampal data spanning 3D ex-vivo histology, ex-vivo 9.4 Tesla MRI, as well as in-vivo structural MRI and resting-state functional MRI (rsfMRI) obtained at 3 and 7 Tesla, together with intracranial encephalography (iEEG) recordings in epilepsy patients. HippoMaps also contains validated tools for spatial map association analysis in the hippocampus that correct for autocorrelation. All code and data are compliant with community standards, and comprehensive online tutorials facilitate broad adoption. Applications of this work span methodologies and modalities, spatial scales, as well as clinical and basic research contexts, and we encourage community feedback and contributions in the spirit of open and iterative scientific resource development.

neuroscience↗

bioRxiv2023

Estimating multimodal brain variability in schizophreniaspectrum disorders: A worldwide ENIGMA study

ObjectiveSchizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Despite its relevance for identifying illness subtypes and informative biomarkers, structural brain heterogeneity in schizophrenia remains incompletely understood. Therefore, the objective of this study was to provide a comprehensive insight into the structural brain heterogeneity associated with schizophrenia. MethodsThis meta- and mega-analysis investigated the variability of multimodal structural brain measures of white and gray matter in individuals with schizophrenia versus healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6139 individuals for a given brain measure, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy. ResultsWe found that individuals with schizophrenia are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Moreover, individuals with schizophrenia showed higher homogeneity of the folding index, especially in the left parahippocampal region. ConclusionsHigher multimodal heterogeneity in frontotemporal regions potentially implies different subtypes of schizophrenia that converge on impaired frontotemporal interaction as a core feature of the disorder. Conversely, more homogeneous folding patterns in the left parahippocampal region might signify a consistent characteristic of schizophrenia shared across subtypes. These findings underscore the importance of structural brain variability in advancing our neurobiological understanding of schizophrenia, and aid in identifying illness subtypes as well as informative biomarkers.

neuroscience↗

bioRxiv2023

Multimodal neural correlates of childhood psychopathology

AO_SCPLOWBSTRACTC_SCPLOWComplex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

neuroscience↗

bioRxiv2022

Cerebellar and cortico-striatal-midbrain contributions to reward-cognition processes and apathy within the psychosis continuum

Negative symptoms in the psychosis continuum are linked to impairments in reward processing and cognitive function. Processes at the interface of reward processing and cognition and their relation to negative symptoms remain little studied, despite evidence suggestive of integration in mechanisms and neural circuitry. Here, we investigated brain activation during reward-dependent modulation of working memory (WM) and their relationship to negative symptoms in subclinical and early stages of the psychosis continuum. We included 27 persons with high schizotypal personality traits and 23 patients with first episode psychosis as well as 27 healthy controls. Participants underwent functional magnetic resonance imaging while performing an established 2-back WM task with two reward levels (5 CHF vs. no reward), which allowed us to assess common reward-cognition regions through whole-brain conjunction analyses and to investigate relations with clinical scores of negative symptoms. As expected for behavior, reward facilitated performance while cognitive load diminished it. At the neural level, the conjunction of high reward and high cognitive load contrasts across the psychosis continuum showed increased hemodynamic activity in the thalamus and the cerebellar vermis. During high cognitive load, more severe apathy but not diminished expression in the psychosis continuum was associated with reduced activity in right lateral orbitofrontal cortex, midbrain, posterior vermal cerebellum, caudate and lateral parietal cortex. Our results suggest that hypoactivity in the cerebellar vermis and the cortical-striatal-midbrain-circuitry in the psychosis continuum relates to apathy possibly via impaired flexible cognitive resource allocation for effective goal pursuit.

neuroscience↗

bioRxiv2021

Multilevel neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

AO_SCPLOWBSTRACTC_SCPLOWIt is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we assessed i) shared dimensions of alterations in cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression, obsessive-compulsive disorder, bipolar disorder, schizophrenia) and ii) carried out a multiscale neural contextualization, by cross-referencing shared anomalies against cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we computed a shared disease dimension on cortical morphology using principal component analysis that described a sensory-fugal pattern with paralimbic regions showing the most consistent abnormalities across conditions. The shared disease dimension was closely related to cortical gradients of microstructure and intrinsic connectivity, as well as neurotransmitter systems, specifically serotonin and dopamine. Our findings embed the shared effects of major psychiatric conditions on brain structure in multiple scales of brain organization and may provide novel insights into neural mechanisms into transdiagnostic vulnerability.

neuroscience↗

bioRxiv2021

Comparing adaptive coding of reward in bipolar I disorder and schizophrenia

BackgroundDeficits in neural processing of reward have been described in both bipolar disorder (BD) and schizophrenia (SZ), but it remains unclear to what extent these deficits are caused by similar mechanisms. Efficient reward processing relies on adaptive coding which allows representing large input spans by limited neuronal encoding ranges. Deficits in adaptive coding of reward have previously been observed across the SZ spectrum and correlated with total symptom severity. In the present work we sought to establish whether adaptive coding is similarly affected in patients with BD. Methods25 patients with BD, 27 patients with SZ and 25 healthy controls performed a variant of the Monetary Incentive Delay task during functional magnetic resonance imaging in two reward range conditions. ResultsAdaptive coding was impaired in BD and SZ in the posterior part of the right caudate. In contrast, BD did not show impaired adaptive coding in the anterior caudate and right precentral gyrus/insula, where SZ showed deficits compared to healthy controls. ConclusionsBD patients show adaptive coding deficits, that are similar to those observed in SZ in the right posterior caudate. Adaptive coding in BD appeared more preserved as compared to SZ participants especially in the more anterior part of the right caudate and to a lesser extent also in the right precentral gyrus. Thus, dysfunctional adaptive coding could constitute a fundamental deficit in severe mental illnesses that extends beyond the schizophrenia spectrum.

neuroscience↗

bioRxiv2021

Schizophrenia polygenic risk during typical development reflects multiscale cortical organization

Schizophrenia is widely recognized as a neurodevelopmental disorder. Abnormal cortical development may by revealed using polygenic risk scoring for schizophrenia (PRS-SCZ). We assessed PRS-SCZ and cortical morphometry in typically developing children (3-21 years) using whole genome genotyping and T1-weighted MRI (n=390) from the Pediatric Imaging, Neurocognition and Genetics (PING) cohort. We contextualise the findings using (i) age-matched transcriptomics, (ii) histologically-defined cytoarchitectural types and functionally-defined networks, (iii) case-control differences of schizophrenia and other major psychiatric disorders. Higher PRS-SCZ was associated with greater cortical thickness, which was most prominent in areas with heightened gene expression of dendrites and synapses. PRS-SCZ related increases in vertex-wise cortical thickness were especially focused in the ventral attention network, while koniocortical type cortex (i.e. primary sensory areas) was relatively conserved from PRS-SCZ related differences. The large-scale pattern of cortical thickness increases related to PRS-SCZ mirrored the pattern of cortical thinning in schizophrenia and mood-related psychiatric disorders. Age group models illustrate a possible trajectory from PRS-SCZ associated cortical thickness increases in early childhood towards thinning in late adolescence, which resembles the adult brain phenotype of schizophrenia. Collectively, combining imaging-genetics with multi-scale mapping, our work provides novel insight into how genetic risk for schizophrenia impacts the cortex early in life.

neuroscience↗

bioRxiv2020

The ENIGMA Toolbox: Cross-disorder integration and multiscale neural contextualization of multisite neuroimaging datasets

Among big data initiatives, the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium--a worldwide alliance of over 2,000 scientists diversified into over 50 Working Groups--has yielded some of the largest studies of the healthy and diseased brain. Integration of multisite datasets to assess transdiagnostic similarities and differences and to contextualize findings with respect to neural organization, however, have been limited. Here, we introduce the ENIGMA Toolbox, a Python/Matlab ecosystem for (i) accessing 100+ ENIGMA datasets, facilitating cross-disorder analysis, (ii) visualizing data on brain surfaces, and (iii) contextualizing findings at the microscale (postmortem cytoarchitecture and gene expression) and macroscale (structural and functional connectomes). Our Toolbox equips scientists with tutorials to explore molecular, histological, and network correlates of noninvasive neuroimaging markers of brain disorders. Moreover, our Toolbox bridges the gap between standardized data processing protocols and analytic workflows and facilitates cross-consortia initiatives. The Toolbox is documented and openly available at http://enigma-toolbox.readthedocs.io. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/423838v3_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@183a3f3org.highwire.dtl.DTLVardef@180d368org.highwire.dtl.DTLVardef@b752a3org.highwire.dtl.DTLVardef@1928c53_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

Determinants 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 and data sharing.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training 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 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.

neuroscience↗

bioRxiv2019

Individual differences in the mechanistic control of the dopaminergic midbrain

The dopaminergic midbrain is associated with brain functions, such as reinforcement learning, motivation and decision-making that are often disturbed in neuropsychiatric disease. Previous research has shown that activity in the dopaminergic midbrain can be endogenously modulated via neurofeedback, suggesting potential for non-pharmacological interventions. However, the robustness of endogenous modulation, a requirement for clinical translation, is unclear. Here, we examined how self-modulation capability relates to regulation transfer. Moreover, to elucidate potential mechanisms underlying successful self-regulation, we studied individual prediction error coding, and, during an independent monetary incentive delay (MID) task, individual reward sensitivity. Fifty-nine participants underwent neurofeedback training either in a veridical or inverted feedback group. Successful self-regulation was associated with post-training activity within the cognitive control network and accompanied by decreasing prefrontal prediction error signals and increased prefrontal reward sensitivity in the MID task. The correlative link of dopaminergic self-regulation with individual differences in prefrontal prediction error and reward sensitivity suggests that reinforcement learning contributes to successful self-regulation. Our findings therefore provide new insights in the control of dopaminergic midbrain activity and pave the way to improve neurofeedback training in neuropsychiatric patients.

neuroscience↗