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Stephan, K. E.

Publications and source records attributed to Stephan, K. E..

At least 19 recordsLinked to original sources

Perceived Control Over Breathing Perturbations Depends on Interosensory Input and Prediction Error

Interoception and homeostatic/allostatic control are not only fundamental for survival but play a key role for maintaining somatic and mental health. Furthermore, metacognitive evaluations of these processes, such as perceived failure of allostatic regulation, have been proposed to underlie the experience of chronic symptoms, such as chronic fatigue. A central question in this regard is what mechanisms the brain uses to evaluate control over bodily states. A prominent computational proposal posits that this is achieved by monitoring of interoceptive prediction errors (PEs). However, to date, this proposal has not been tested experimentally. Here, we tested this hypothesis by applying computational process models of perceived control to data from a novel behavioural paradigm, the Respiratory Metacognition of Control Task (RMCT). The RMCT manipulates control over breathing by changing inspiratory resistive loads as a function of control achieved in a gamified prediction task. We developed and compared trial-by-trial generative models of perceived explicit control in the RMCT, using data from 50 volunteers in a pre-registered analysis. Bayesian model selection suggested that perceived control over breathing is best explained as a function of both trial-wise interoceptive outcomes (breathing with or without an inspiratory resistive load) and trial-wise PEs about respiratory resistance. These results support a longstanding computational proposal of how the brain detects failures of bodily regulation and provide a mechanistic model for understanding inter-individual differences in perceived control over bodily states.

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Metabolic state and energy reserve dynamically shape human decision-making

Animals continuously adapt their behaviour to changing physiological needs through flexible regulation of behavioural control. However, how the energetic state shapes the distinct components of this adaptive control in humans remains poorly understood. Here, we tested how fasting-induced energy deficits and stored energy reserves jointly regulate inhibitory control and incentive motivation in healthy participants. Fasting selectively increased impulsivity for food rewards, an effect attenuated by body fat percentage. In contrast, fasting increased effort expenditure across both food and monetary rewards, revealing a domain-general increase in motivation best explained by relative energy deficit--the ratio of energy expended during fasting to stored energy reserves. Neither effect was explained by changes in subjective reward valuation. Although self-report questionnaires captured stable behavioural tendencies, they failed to account for state-dependent behavioural variation. Together, these findings show that inhibitory control and motivation are governed by distinct metabolic principles operating across different timescales, revealing that adaptive behavioural control depends on the energetic significance of metabolic state rather than on fasting alone.

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Differences in layer-specific activation of the insula during interoceptive vs. exteroceptive attention

Attention modulates the relative weighting of information and plays a critical role in modern theories of perception. While its role in exteroceptionthe perception of the external environmenthas been extensively studied, attentional processes in interoception, the perception of bodily states, are less well understood. In this study, we exploited high-field (7 Tesla) functional magnetic resonance imaging and concurrent field monitoring to investigate interoceptive attention at the level of cortical layers during a heartbeat attention task. Voxel-wise analyses reveal increased activity in the bilateral dorsal mid-insula during interoceptive attention (attending to ones heartbeat) compared to exteroceptive attention (attending to auditory white noise). Layer-specific analyses further demonstrate that this difference in activity is significantly higher in upper compared to lower cortical layers. This activation pattern persists after accounting for potential vascular artifacts through a deconvolution analysis with a physiological point spread function (PSF). To our knowledge, these findings represent the first empirical demonstration of layer-specific processes during interoceptive attention in human cortex. They may prove useful to inform and constrain theories of computational principles and physiological implementation of interoception.

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Test-retest reliability of auditory MMN measured with OPM-MEG

In this paper, we report results from an investigation of auditory mismatch responses as measured by magnetoencephalography (MEG) based on optically pumped magnetometers (OPM). Specifically, as part of a quality control study, we examined the reliability and validity of auditory mismatch negativity (MMN) recordings, obtained with a newly installed OPM-MEG system. Based on OPM-MEG data from 30 healthy volunteers, measured twice with an established auditory MMN paradigm with frequency deviants, we examined the following questions: First, we focused on construct validity and examined whether OPM-MEG measurements of MMN responses (in terms of event-related fields, ERFs) were qualitatively comparable to previous MMN findings from studies using EEG or MEG based on superconducting quantum interference devices (SQUIDs). In particular, we examined whether significant MMN responses measured by OPM-MEG occurred in a comparable time window and showed a similar topography as in previous EEG/MEG studies of MMN. Second, we quantified test-retest reliability of MMN amplitude and latency over two separate measurement sessions. The results of our analyses show that MMN responses recorded with OPM-MEG are in good agreement with previously reported MMN results in terms of timing and topography. Furthermore, the comparison of group-level MMN topographies and timeseries shows excellent consistency across the two measurement sessions. Our quantitative test-retest reliability analyses at the sensor level indicate good reliability for MMN amplitude, but poor reliability for MMN latency. Overall, our findings suggest that OPM-MEG measurements of auditory MMN (i) are comparable to results from EEG and SQUID-based MEG and (ii) show good test-retest reliability for amplitude measures at the sensor level. Notably, these results were achieved in an "out of the box" state of the OPM-MEG system, shortly after installation and without further optimisation. The reason for the insufficient reliability for MMN latency we observed is currently under investigation and represents an important target for future improvements.

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Thermoceptive predictions and prediction errors in the anterior insula

Contemporary theories of interoception propose that the brain constructs a model of the body for predicting the states and allostatic needs of all organs, including the skin, and updates this model using prediction error signals. However, empirical tests of this proposal are scarce in humans. This computational neuroimaging study investigated the presence and location of thermoceptive predictions and prediction errors in the brain using probabilistic manipulations of skin temperature in a novel interoceptive learning paradigm. Using functional MRI in healthy volunteers, we found that a Bayesian model provided a better account of participants skin temperature predictions than a non-Bayesian model. Further, activity in a network including the anterior insula was associated with trial-wise predictions and precision-weighted prediction errors. Our findings provide further evidence that the anterior insula plays a key role in implementing the brains model of the body, and raise important questions about the structure of this model.

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Bayesian Workflow for Generative Modeling in Computational Psychiatry

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that harnessing information from two different data streams (binary responses and continuous response times) improves the accuracy of inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.

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Emotion-induced frontal α asymmetry predictsrelapse after discontinuation of antidepressantmedication

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSOne in three patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of Major Depressive Disorder (MDD). However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by fMRI has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor. MethodsPatients (n=56) who had remitted from a depressive episode while taking antidepressants underwent EEG recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a six-months follow-up period. 35 healthy controls were also tested. Current source density of the EEG power in the band (8-13Hz) was extracted and alpha-asymmetry was computed by comparing the power across two hemispheres at frontal electrodes (F5 and F6). OutcomesSad mood induction was robust across all groups. Reactivity of -asymmetry to sad mood did not distinguish healthy controls from patients with remitted MDD on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in -asymmetry compared to patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy). InterpretationA simple EEG-based measure of emotional reactivity may have clinical utility in the management of antidepressant discontinuation. FundingSwiss National Science Foundation project grant 320030L_153449 / 1 to QJMH, Stiftung Deutsche Depressionshilfe to HW and QJMH, a Deutsche Forschungsgemeinschaft (DFG) grant (WA 1539/5-1) to HW, EMDO Stiftung to QJMH and the Rene and Susanne Braginsky Foundation and Clinical Research Priority Programme "Molecular Imaging" at the University of Zurich to KES.

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Incorporating uncertainty within dynamic interoceptive learning

Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. Here we extended an interoceptive Breathing Learning Task (BLT) to incorporate continuous measures of prediction certainty, and tested its application using a Rescorla Wagner (RW) associative learning model. Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances. The task was modified from a previous version and required continuous, rather than binary predictions, in order to include a more precise measure of prediction certainty. The RW model was used to fit a learning rate to each participants continuous and binarised predictions, and was additionally extended to test whether learning rates differed according to stimuli valence. The empirical task data demonstrated excellent replicability compared to previously collected data using binary predictions, and the continuous model fits closely captured participant behaviour at the group level. The model extension to estimate different learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) trials indicated that learning rates did not significantly differ according to stimuli nature. Furthermore, examining the relationship between estimates of prediction certainty and learning rates with interoceptive and mental health questionnaires demonstrated that fatigue severity was related to both prediction certainty and learning rate, and anxiety sensitivity was related to prediction certainty. The updated task and model show promise for future investigations into interoceptive learning and potential links to mental health.

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Test-retest reliability of regression dynamic causal modeling

AO_SCPLOWBSTRACTC_SCPLOWRegression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability - a test-theoretical property of particular importance for clinical applications - together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project (HCP) dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean ICC 0.24-0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates to measures of functional connectivity, rDCM performed favorably - particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications. AO_SCPLOWUTHORC_SCPLOW SO_SCPLOWUMMARYC_SCPLOWTest-retest reliability is an important prerequisite for the validity of connectivity estimates in many situations, particularly in clinical applications. Here, using different datasets from the Human Connectome Project, we demonstrate that regression DCM (rDCM) yields good to excellent test-retest reliability when focusing on strong connections. Comparing this to the test-retest reliability of functional connectivity measures, rDCM performed favorably in most cases. Furthermore, we show that reliability is not homogeneously distributed: we identified several regions (primarily in frontal and temporal lobe) that were linked via highly-reliable connections, regardless of the paradigm. Finally, we demonstrate that individual connectivity profiles are sufficiently unique that participants can be identified with high accuracy. Our findings emphasize the potential of rDCM for robust inference on directed "connectivity fingerprints" from fMRI data.

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Interoception of breathing and its relationship with anxiety

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. While interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modelling and high-field (7 Tesla) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low vs. moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multimodal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.

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Optogenetic Activation of Striatal D1/D2 Medium Spiny Neurons Differentially Engages Downstream Connected Areas Beyond the Basal Ganglia

The basal ganglia (BG) are a group of subcortical nuclei responsible for motor control, motor learning and executive function. Central to BG function are striatal medium spiny neurons (MSNs) expressing D1 and D2 dopamine receptors. D1 and D2 MSNs are typically considered functional antagonists that facilitate voluntary movements and inhibit competing motor patterns, respectively. While their opposite role is well documented for certain sensorimotor loops of the BG-thalamocortical network, it is unclear whether MSNs maintain a uniform functional role across the striatum and which influence they exert on brain areas outside the BG. Here, we addressed these questions by combining optogenetic activation of D1 and D2 MSNs in the mouse ventrolateral caudoputamen (vl CPu) with whole-brain functional MRI (fMRI) recordings. Neuronal excitation of either cell population in the vl CPu evoked distinct activity patterns in key regions of the BG-thalamocortical network including the pallidum, thalamus and motor cortex. Importantly, we report that striatal D1 and D2 MSN stimulation differentially engaged cerebellar and prefrontal regions. We characterised these long-range interactions by computational modelling of effective connectivity and confirmed that changes in D1 / D2 output drive functional relationships between regions within and beyond the BG. These results suggest a more complex functional organization of MSNs across the striatum than previously anticipated and provide evidence for the existence of an interconnected fronto - BG - cerebellar network modulated by striatal D1 and D2 MSNs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=129 HEIGHT=200 SRC="FIGDIR/small/436576v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@a18349org.highwire.dtl.DTLVardef@1b256faorg.highwire.dtl.DTLVardef@16c454aorg.highwire.dtl.DTLVardef@1392b23_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Auditory mismatch responses are differentially sensitive to changes in muscarinic acetylcholine versus dopamine receptor function

The auditory mismatch negativity (MMN) has been proposed as a biomarker of NMDA receptor (NMDAR) dysfunction in schizophrenia. Pathophysiological theories suggest that such dysfunction might be partially caused by aberrant interactions of different modulatory neurotransmitters with NMDARs, which could explain heterogeneity among patients with schizophrenia and their treatment response. Understanding the differential impact of different neuromodulators on readouts of NMDAR function is therefore of high clinical relevance. Here, we report results from two studies (N=81 each) which systematically tested whether the MMN is sensitive to diminishing and enhancing cholinergic vs. dopaminergic function. Both studies used a double-blind, placebo-controlled between-subject design and monitored individual drug plasma levels. Using a novel variant of the auditory oddball paradigm, we contrasted phases with stable versus volatile probabilities of tone switches. In the first study, we found that the muscarinic acetylcholine receptor antagonist biperiden reduced and/or delayed mismatch responses, particularly during stable phases of the experiment, whereas this effect was absent for amisulpride, a dopamine D2/D3 receptor antagonist. The direct comparison between biperiden and amisulpride indicated a significant drug x mismatch interaction. In the second study, neither elevating acetylcholine nor dopamine levels via administration of galantamine and levodopa, respectively, exerted significant effects on MMN. Overall, our results indicate differential sensitivity of the MMN to changes in cholinergic (muscarinic) versus dopaminergic receptor function. This finding may prove useful for developments of future tools for predicting individual treatment responses in disorders that show abnormal MMN, such as schizophrenia.

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TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

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Technical Note: A fast and robust integrator of delay differential equations in DCM for electrophysiological data

Dynamic causal models (DCMs) of electrophysiological data allow, in principle, for inference on hidden, bulk synaptic function in neural circuits. The directed influences between the neuronal elements of modeled circuits are subject to delays due to the finite transmission speed of axonal connections. Ordinary differential equations are therefore not adequate to capture the ensuing circuit dynamics, and delay differential equations (DDEs) are required instead. Previous work has illustrated that the integration of DDEs in DCMs benefits from sophisticated integration schemes in order to ensure rigorous parameter estimation and correct model identification. However, integration schemes that have been proposed for DCMs either emphasise speed (at the possible expense of accuracy) or robustness (but with computational costs that are problematic in practice). In this technical note, we propose an alternative integration scheme that overcomes these shortcomings and offers high computational efficiency while correctly preserving the nature of delayed effects. This integration scheme is available as open-source code in the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) toolbox and can be easily integrated into existing software (SPM) for the analysis of DCMs for electrophysiological data. While this paper focuses on its application to the convolution-based formalism of DCMs, the new integration scheme can be equally applied to more advanced formulations of DCMs (e.g. conductance based models). Our method provides a new option for electrophysiological DCMs that offers the speed required for scientific projects, but also the accuracy required for rigorous translational applications, e.g. in computational psychiatry.

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A Hilbert-based method for processing respiratory timeseries

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation will be publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (translationalneuromodeling.org/tapas). HighlightsO_LIWe introduce a new estimator for respiratory volume per unit time from respiratory recordings. C_LIO_LIWe demonstrate how this is able to accurately characterise atypical breathing events. C_LIO_LIThis removes significantly more variance when used as a confound regressor for fMRI data. C_LIO_LIOur implementation will be included in PhysIO, released as part of TAPAS: translationalneuromodeling.org/tapas. C_LI

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Regression dynamic causal modeling for resting-state fMRI

AO_SCPLOWBSTRACTC_SCPLOW"Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI - regression dynamic causal modeling (rDCM) - extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

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Dynamic causal modeling of layered magnetoencephalographic event-related responses

The layered architecture of cortex is thought to play a fundamental role in shaping cortical computations. However, direct electrophysiological measurements of layered activity are not possible non-invasively in humans. Recent advances have shown that a distinction of two layers can be achieved using magnetoencephalography in combination with head casts and advanced spatial modeling. In this technical note, we present a dynamic causal model of a single cortical microcircuit that models event related potentials. The model captures the average dynamics of a detailed two layered circuit. It combines a temporal model of neural dynamics with a spatial model of a layer specific lead field to facilitate layer separation. In simulations we show that the spatial arrangement of the two layers can be successfully recovered using Bayesian inference. The layered model can also be distinguished from a single dipole model. We conclude that precision magnetoencephalography in combination with detailed dynamical system modeling can be used to study non-invasively the fast dynamics of layered computations.

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The Filter Detection Task for measurement of breathing-related interoception and metacognition

The study of the brains processing of sensory inputs from within the body ( interoception) has been gaining rapid popularity in neuroscience, where interoceptive disturbances are thought to exist across a wide range of chronic physiological and psychological conditions. Here we present a task and analysis procedure to quantify specific dimensions of breathing-related interoception, including interoceptive sensitivity (accuracy), decision bias, metacognitive bias, and metacognitive performance. Two major developments address some of the challenges presented by low trial numbers in interoceptive experiments: (i) a novel adaptive algorithm to maintain task performance at 70-75% accuracy; (ii) an extended hierarchical metacognitive model to estimate regression parameters linking metacognitive performance to relevant (e.g. clinical) variables. We demonstrate the utility of the task and analysis developments, using both simulated data and three empirical datasets. This methodology represents an important step towards accurately quantifying interoceptive dimensions from a simple experimental procedure that is compatible with clinical settings.

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