Publications

{The inference of causal relationships using observational data from partially observed multivariate systems with hidden variables is a fundamental question in many scientific domains. Methods extracting causal information from conditional independencies between variables of a system are common tools for this purpose, but are limited in the lack of independencies. To surmount this limitation, we capitalize on the fact that the laws governing the generative mechanisms of a system often result in substructures embodied in the generative functional equation of a variable, which act as sufficient statistics for the influence that other variables have on it. These functional sufficient statistics constitute intermediate hidden variables providing new conditional independencies to be tested. We propose to use the Information Bottleneck method, a technique commonly applied for dimensionality reduction, to find underlying sufficient sets of statistics. Using these statistics we formulate new additional rules of causal orientation that provide causal information not obtainable from standard structure learning algorithms, which exploit only conditional independencies between observable variables. We validate the use of sufficient statistics for structure learning both with simulated systems built to contain specific sufficient statistics and with benchmark data from regulatory rules previously and independently proposed to model biological signal transduction networks.}

{Goal-directed behavior requires the interaction of multiple brain regions. How these regions and their interactions with brain-wide activity drive action selection is less understood. We have investigated this question by combining whole-brain volumetric calcium imaging using light-field microscopy and an operant-conditioning task in larval zebrafish. We find global, recurring dynamics of brain states to exhibit pre-motor bifurcations toward mutually exclusive decision outcomes. These dynamics arise from a distributed network displaying trial-by-trial functional connectivity changes, especially between cerebellum and habenula, which correlate with decision outcome. Within this network the cerebellum shows particularly strong and predictive pre-motor activity (\textgreater10 s before movement initiation), mainly within the granule cells. Turn directions are determined by the difference neuroactivity between the ipsilateral and contralateral hemispheres, while the rate of bi-hemispheric population ramping quantitatively predicts decision time on the trial-by-trial level. Our results highlight a cognitive role of the cerebellum and its importance in motor planning.}

{To determine own upright body orientation the brain creates a sense of verticality by a combination of multisensory inputs. To test whether this process is affected by aging, we placed younger and older adults on a motion platform and systematically tilted the orientation of their visual surroundings by using an augmented reality setup. In a series of trials, participants adjusted the orientation of the platform until they perceived themselves to be upright. Tilting the visual scene around the roll axis induced a bias in subjective postural vertical determination in the direction of scene tilt in both groups. In the group of older participants, however, the observed peak bias was larger and occurred at larger visual tilt angles. This indicates that the susceptibility to visually induced biases increases with age, possibly caused by a reduced reliability of sensory information.}

{We developed a nanosized perfluorocarbon-based system with incorporated paramagnetic Gd(iii) chelates, able to generate a quantitative 19F MRI signal, while in parallel capable of modulating the 1H MRI signal in response to the coordination of Ca2+ ions. Subsequently, we performed experiments in vivo and estimated the concentration of the applied probe in the tissue by means of 19F MRI.}

{How do people perceive and communicate structure? We investigate this question by letting participants play a communication game, where one player describes a pattern, and another player redraws it based on the description alone. We use this paradigm to compare two models of pattern description, one compositional (complex structures built out of simpler ones) and one non-compositional. We find that compositional patterns are communicated more effectively than non-compositional patterns, that a compositional model of pattern description predicts which patterns are harder to describe, and that this model can be used to evaluate participants\textquoteright drawings, producing human-like quality ratings. Our results suggest that natural language can tap into a compositionally structured pattern description language.}

{Computational psychiatry is a nascent field that attempts to use multi-level analyses of the underlying computational problems that we face in navigating a complex, uncertain and changing world to illuminate mental dysfunction and disease. Two particular foci of the field are the costs and benefits of environmental adaptivity and the danger and necessity of heuristics. Here, we examine the extent to which these foci and others can be used to study the actual and potential flaws of the artificial computational devices that we are increasingly inventing and empowering to navigate this very same environment on our behalf.}

{The insula has been classically divided into vast granular, dysgranular and agranular sectors. Over the years, several distinct studies proposed subdivisions of these sectors, with however no consensus. We recently proposed a cyto- and myelo-architectonic partition in which each sector contained sharply delimited areas (Evrard et al. 2014 J Comp Neurol 522: 64-97). Some of these areas were further divided into distinct subareas with obvious functional implications. Here, we examined the spatial relationship between architectonic boundaries and tract-tracing labeling in the insula in the macaque monkey. Injections of neuronal tracers in distinct areas of the prefrontal or anterior cingulate cortices produced heterogeneous and discontinuous patterns of anterograde and retrograde labeling in the insula. These patterns were made of sharply delimited patches forming anteroposterior stripes across consecutive coronal sections. While the overall pattern of labeling varied with the injection site, the patches systematically coincided with specific architectonic subareas, particularly in the dysgranular insula. This unequivocally validates our prior architectonic partition and strongly supports the idea of a refined modular Bauplan of the primate insula. This modular organization may underlie a serial stream of integration of interoception with \textquoteleftself-agency\textquoteright and \textquoteleftsocial\textquoteright activities across distinct insulo-prefrontal processing units that need to be explored.}

{Radio-frequency (rf) coils are used in all clinical and research magnetic-resonance-imaging (MRI) systems to excite nuclear spins and to receive signals from them. The quality of imaging depends strongly on the signal-to-noise ratio (SNR) and the transmit efficiency of the coils. The birdcage volume coils used in most MRI scanners for homogeneous imaging of a sample are typically shielded from the external systems of the MRI scanner, i.e., the gradient coils, to confine the rf field within the region of interest. However, the near magnetic field of a conventional copper rf shield surrounding a birdcage coil interferes destructively with the primary field of the coil in the sample, which significantly limits the SNR and the transmit efficiency. In the work presented here, we theoretically study and experimentally demonstrate the possibility of creating an artificial magnetic rf shield for a birdcage coil with constructive interference in a sample. This effect is similar to the in-phase reflection of antenna far fields from a magnetic shield but affects the near field in MRI. We build an analytical model of a birdcage coil shielded with a cylindrical impedance boundary and analyze the conditions for increasing the efficiency of the coil by means of the shield. We conclude that by replacing a copper shield with an artificial magnetic one, it is possible to reduce the dissipative intrinsic losses of the coil and increase the power absorbed by the sample, which improves the efficiency. To demonstrate the effect, we perform a detailed numerical simulation and an experiment with a small birdcage in a 7-T 19F MRI system with a magnetic shield implemented as a periodic cylindrical metal structure with corrugations filled with a ceramic.}

{Even when we are wearing gloves, we can easily detect whether a surface that we are touching is sticky or not. However, we know little about the similarities between brain activations elicited by this glove contact and by direct contact with our bare skin. In this functional magnetic resonance imaging (fMRI) study, we investigated which brain regions represent stickiness intensity information obtained in both touch conditions, i.e., skin contact and glove contact. First, we searched for neural representations mediating stickiness for each touch condition separately and found regions responding to both mainly in the supramarginal gyrus and the secondary somatosensory cortex. Second, we explored whether surface stickiness is encoded in common neural patterns irrespective of how participants touched the sticky stimuli. Using a cross-condition decoding method, we tested whether the stickiness intensities could be decoded from fMRI signals evoked by skin contact using a classifier trained on the responses elicited by glove contact, and vice versa. Our results found shared neural encoding patterns in the bilateral angular gyri and the inferior frontal gyrus (IFG) and suggest that these areas represent stickiness intensity information regardless of how participants touched the sticky stimuli. Interestingly, we observed that neural encoding patterns of these areas were reflected in participants\textquoteright intensity ratings. This study revealed common and distinct brain activation patterns of tactile stickiness using two different touch conditions, which may broaden the understanding of neural mechanisms related to surface texture perception.}

{Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision. While previous work has focused on exploiting statistical independence to \textit\textbraceleftdisentangle\textbraceright latent factors, we argue that such requirement can be advantageously relaxed and propose instead a non-statistical framework that relies on identifying a modular organization of the network, based on counterfactual manipulations. Our experiments support that modularity between groups of channels is achieved to a certain degree on a variety of generative models. This allowed the design of targeted interventions on complex image datasets, opening the way to applications such as computationally efficient style transfer and the automated assessment of robustness to contextual changes in pattern recognition systems.}

{Multiple theories attribute to the primate prefrontal cortex a critical role in conscious perception. However, opposing views caution that prefrontal activity could reflect other cognitive variables during paradigms investigating consciousness, such as decision-making, monitoring and motor reports. To resolve this ongoing debate, we recorded from prefrontal ensembles of macaque monkeys during a no-report paradigm of binocular rivalry that instigates internally driven transitions in conscious perception. We could decode the contents of consciousness from prefrontal ensemble activity during binocular rivalry with an accuracy similar to when these stimuli were presented without competition. Oculomotor signals, used to infer conscious content, were not the only source of these representations since visual input could be significantly decoded when eye movements were suppressed. Our results suggest that the collective dynamics of prefrontal cortex populations reflect internally generated changes in the content of consciousness during multistable perception.}

{Dipole antennas have been successfully utilized at ultra-high fields (UHF, \textgreater7 T) as elements of human body arrays. Usage of dipoles for UHF human head arrays is still under development. In this case, dipoles must be made much shorter, and placed at a relatively large distance to the head. As a result, dipoles are not well loaded and are often purely decoupled. In this work, we developed a novel method of decoupling of adjacent dipole antennas, and used this technique while constructing a novel 9.4 T human head TxRx dipole array coil. The array demonstrates good decoupling and full-brain coverage.}

{Dipole antennas have recently been introduced to the field of MRI and successfully used, mostly as elements of ultra-high field (UHF, $\geq$ 7 T) human body arrays. Usage of dipole antennas for UHF human head transmit (Tx) arrays is still under development. Due to the substantially smaller size of the sample, dipoles must be made significantly shorter than in the body array. Additionally, head Tx arrays are commonly placed on the surface of rigid helmets made sufficiently large to accommodate tight-fit receive arrays. As a result, dipoles are not well loaded and are often poorly decoupled, which compromises Tx efficiency. Commonly, adjacent array elements are decoupled by circuits electrically connected to them. Placement of such circuits between distantly located dipoles is difficult. Alternatively, decoupling is provided by placing passive antennas between adjacent dipole elements. This method only works when these additional components are sufficiently small (compared with the size of active dipoles). Otherwise, RF fields produced by passive elements interfere destructively with the RF field of the array itself, and previously reported designs have used passive dipoles of about the size of array dipoles. In this work, we developed a novel method of decoupling for adjacent dipole antennas, and used this technique while constructing a 9.4 T human head eight-element transceiver array. Decoupling is provided without any additional circuits by simply folding the dipoles and using an RF shield located close to the folded portion of the dipoles. The array reported in this work demonstrates good decoupling and whole-brain coverage.}

{Oncolytic virotherapy (OV) is a promising treatment for high mortality cancers.1Non-invasive monitoring of OV is essential for optimizing the clinical outcome and providing an improved understanding of the interactions between the virus and its tumor-host. Chemical exchange saturation transfer (CEST) MRI is a molecular imaging technique that may shed new light on OV, as it is capable of detecting pro-tein concentration and pH changes. However, clinical translation of CEST methods has been hindered by the qualitative nature of the image contrast and the long image acquisition times.}

{Prominent asymmetries in the bSSFP frequency profile in tissues with distinct fiber pathways are known to be a confounding factor in the quantification of relaxation times from a series of phase-cycled scans. It has been demonstrated that the resulting bias can be eliminated by training artificial neural networks using gold standard relaxation times as target. Here, the ability of neural networks to not only provide gold standard brain tissue T1 and T2 values as well as field map estimates (B1, $\Delta$B0) but also to highly accelerate the acquisition by reducing the number of phase-cycles is explored.}

{Purpose: Calculation of sophisticated MR contrasts often requires complex mathematical modeling. Data evaluation is computationally expensive, vulnerable to artifacts, and often sensitive to fit algorithm parameters. In this work, we investigate whether neural networks can provide not only fast model fitting results, but also a quality metric for the predicted values, so called uncertainty quantification, investigated here in the context of multi-pool Lorentzian fitting of CEST MRI spectra at 3T. Methods A deep feed-forward neural network including a probabilistic output layer allowing for uncertainty quantification was set up to take uncorrected CEST-spectra as input and predict 3T Lorentzian parameters of a 4-pool model (water, semisolid MT, amide CEST, NOE CEST), including the B0 inhomogeneity. Networks were trained on data from 3 subjects with and without data augmentation, and applied to untrained data from 1 additional subject and 1 brain tumor patient. Comparison to conventional Lorentzian fitting was performed on different perturbations of input data. Results The deepCEST 3T networks provided fast and accurate predictions of all Lorentzian parameters and were robust to input perturbations because of noise or B0 artifacts. The uncertainty quantification detected fluctuations in input data by increase of the uncertainty intervals. The method generalized to unseen brain tumor patient CEST data. Conclusions The deepCEST 3T neural network provides fast and robust estimation of CEST parameters, enabling online reconstruction of sophisticated CEST contrast images without the typical computational cost. Moreover, the uncertainty quantification indicates if the predictions are trustworthy, enabling confident interpretation of contrast changes.}

{Analysis of CEST data often requires complex mathematical modeling before contrast generation, which can be error prone and time-consuming. Here, a probabilistic deep learning approach is introduced to shortcut conventional Lorentzian fitting analysis of 3T in-vivo CEST data by learning from previously evaluated data. It is demonstrated that the trained networks generalize to data of a healthy subject and a brain tumor patient, providing CEST contrasts in a fraction of the conventional evaluation time. Additionally, the probabilistic network architecture enables uncertainty quantification, indicating if predictions are trustworthy, which is assessed by perturbation analysis.}

{Different classes of small- or nano-sized calcium-sensitive probes for magnetic resonance imaging (MRI) have been proposed in the last two decades. These compounds have been developed mainly for functional MRI purposes and tested in vivo in different animal models. Most of them are paramagnetic systems that change their relaxivity in the presence of the divalent ion calcium, resulting in increased T1 or T2 contrast. In this work, we report the investigation of their relaxometric behavior at low magnetic fields, specifically the comparison of the monomeric Ca-sensitive probe and the corresponding dendrimer conjugates of generations 0, 1 and 2 (G0, G1 and G2, respectively). As a result, a relaxivity hump between 10 and 100 MHz of the Larmor frequency progressively appeared with an increase in the size of the investigated contrast agent, indicative of a restricted rotational motion of the complexes as long as the size of the molecule increases. The same trend with a more pronounced effect was detectable in the presence of calcium. The relaxivity enhancement for the Ca2+ adducts, primarily caused by an increase of the hydration state of Gd3+, went from ca. 130\textpercent for the monomeric probe to ca. 310\textpercent for the G2 dendrimer conjugate at 0.5 T and 25 \mbox{$^\circ$}C. T1 weighted magnetic resonance images acquired at 1 T displayed the strong ability of these systems to change their contrast according to the presence of calcium at this field, thus laying the basis for promising future in vivo applications.}

{Dorsal human midbrain contains two nuclei with clear laminar organization, the superior and inferior colliculi. These nuclei extend in depth between the superficial dorsal surface of midbrain and a deep midbrain nucleus, the periaqueductal gray matter (PAG). The PAG, in turn, surrounds the cerebral aqueduct (CA). This study examined the use of two depth metrics to characterize depth and thickness relationships within dorsal midbrain using the superficial surface of midbrain and CA as references. The first utilized nearest-neighbor Euclidean distance from one reference surface, while the second used a level-set approach that combines signed distances from both reference surfaces. Both depth methods provided similar functional depth profiles generated by saccadic eye movements in a functional MRI task, confirming their efficacy for delineating depth for superficial functional activity. Next, the boundaries of the PAG were estimated using Euclidean distance together with elliptical fitting, indicating that the PAG can be readily characterized by a smooth surface surrounding PAG. Finally, we used the level-set approach to measure tissue depth between the superficial surface and the PAG, thus characterizing the variable thickness of the colliculi. Overall, this study demonstrates depth-mapping schemes for human midbrain that enables accurate segmentation of the PAG and consistent depth and thickness estimates of the superior and inferior colliculi.}

Spreading processes are conventionally monitored on a macroscopic level by counting the number of incidences over time. The spreading process can then be modeled either on the microscopic level, assuming an underlying interaction network, or directly on the macroscopic level, assuming that microscopic contributions are negligible. The macroscopic characteristics of both descriptions are commonly assumed to be identical. In this work we show that these characteristics of microscopic and macroscopic descriptions can be different due to coalescence, i.e., a node being activated at the same time by multiple sources. In particular, we consider a (microscopic) branching network (probabilistic cellular automaton) with annealed connectivity disorder, record the macroscopic activity, and then approximate this activity by a (macroscopic) branching process. In this framework we analytically calculate the effect of coalescence on the collective dynamics. We show that coalescence leads to a universal nonlinear scaling function for the conditional expectation value of successive network activity. This allows us to quantify the difference between the microscopic model parameter and established estimates of the macroscopic branching parameter. To overcome this difference, we propose a nonlinear estimator that correctly infers the microscopic model parameter for all system sizes.

{Purpose The purpose of this study is to introduce a novel design method of a shim coil array specifically optimized for whole brain shimming and to compare the performance of the resulting coils to conventional spherical harmonic shimming. Methods The proposed design approach is based on the stream function method and singular value decomposition. Eighty-four field maps from 12 volunteers measured in seven different head positions were used during the design process. The cross validation technique was applied to find an optimal number of coil elements in the array. Additional 42 field maps from 6 further volunteers were used for an independent validation. A bootstrapping technique was used to estimate the required population size to achieve a stable coil design. Results Shimming using 12 and 24 coil elements outperforms fourth- and fifth-order spherical harmonic shimming for all measured field maps, respectively. Coil elements show novel coil layouts compared to the conventional spherical harmonic coils and existing multi-coils. Both leave-one-out and independent validation demonstrate the generalization ability of the designed arrays. The bootstrapping analysis predicts that field maps from approximately 140 subjects need to be acquired to arrive at a stable design. Conclusions The results demonstrate the validity of the proposed method to design a shim coil array matched to the human brain anatomy, which naturally satisfies the laws of electrodynamics. The design method may also be applied to develop new shim coil arrays matched to other human organs.}

{Introduction: Patients with functional (psychogenic) movement disorders (FMD) can have various motor and non-motor symptoms including anxiety and depression. An aberrant integration of the sensorimotor and affective processing, and a dysfunctional sense of agency have been proposed as the key mechanisms involved in FMD development suggesting that FMD have features of a multiple network disorder. Therefore, exploration of brain connectivity mapping could provide important insights in the pathophysiology of FMD. The aim of this study was to analyze resting state fMRI to avoid variability related to aberrant movements or somatosensory misperception while taking into account anxiety and depression as confounding factors. To avoid selection bias, we used the eigen-vector centrality (EC) mapping to allow data-driven detection of connectivity hubs with rich connections to other regions of the brain that are highly connected. Methods: Forty-four patients with FMD (34F, age 45$\pm$(SD)9 years, disease duration 8.4$\pm$5,6 years) with heterogeneous motor phenotypes were compared to 44 matched control subjects (33F, 44$\pm$10y). The anxiety state (STAIX-1) and trait (STAIX-2) and Beck depression inventory (BDI-II) were recorded. All subjects were instructed to watch a cross for 10 minutes during rs-fMRI acquisition (3T, Siemens, Skyra: T2\textasteriskcentered-weighted gradient echo echo-planar imaging, TR\textequals2s, TE\textequals30ms, FA\textequals90, 300 scans). The preprocessing and statistics were performed with the SPM12 software (London, UK). General connectivity was derived from EC mapping (Lohmann et al., 2010) with a new correlation metric called ReLU correlation (Lohmann et al., 2018). For comparison, we also computed the EC with three other correlation metrics. Group analysis was based on a GLM implementing the contrast between both groups of subjects including \textquotesingleSTAIX-2\textquotesingle, \textquotesingleage\textquotesingle, \textquotesinglegender\textquotesingle, and \textquotesingleantidepressant drugs\textquotesingle as additional covariates. For selective connectivity, voxel-wise correlations using seeds derived from the results of the group EC analysis were computed in each subject. Group results were obtained with non-parametric statistics based on threshold-free cluster enhancement (TFCE toolbox, Jena, Germany) using the family-wise error correction at p\textless0.05. Results: FMD patients in comparison with controls showed decreased general connectivity in the SMA and midcingulate bilaterally and in the right superior frontal gyrus and right insula. This result became significant if the STAIX-2 was used as the nuisance covariate in the model (Figure 1). Selective connectivity from the seeds in the SMA and midcingulate showed disconnections in FMD patients in comparison with controls in multiple regions involving Rolandic areas, superior temporal gyri, posterior cingulate, insula, inferolateral prefrontal cortex, visual cortex and cerebellum (Figure 2). We obtained similar results with all correlation metrics, however, the RLC approach appeared to be most sensitive for the detection of EC differences between both groups. Conclusions: Despites motor symptoms variability, the brain of FMD patients exhibited a uniform connectivity pattern involving the SMA and midcingulate, which were generally less connected with all other cortical and subcortical regions than in controls. The results are in line with previous reports (Roelofs et al., 2019) using different approaches to rs-fMRI or brain morphometry analyses and fit well to our knowledge of the functional role of these regions and their possible malfunctions. Disconnected SMA is potentially associated with dysfunctional planning of voluntary movements or with insufficient suppression of unwanted actions (Nachev et al., 2008; Ruan et al., 2018). Disconnection of the midcingulate may affect multisensory orientation, nociceptive processing or nocifensive behavior (Vogt, 2016; Ospina et al., 2019). Our findings thus support FMD as a network disorder affecting hubs connecting motor and non-motor networks. Supported by the grant AZV 16-29651A.}

{Individual characteristics in human decision-making are often quantified by fitting a parametric cognitive model to subjects\textquotesingle behavior and then studying differences between them in the associated parameter space. However, these models often fit behavior more poorly than recurrent neural networks (RNNs), which are more flexible and make fewer assumptions about the underlying decision-making processes. Unfortunately, the parameter and latent activity spaces of RNNs are generally high-dimensional and uninterpretable, making it hard to use them to study individual differences. Here, we show how to benefit from the flexibility of RNNs while representing individual differences in a low-dimensional and interpretable space. To achieve this, we propose a novel end-to-end learning framework in which an encoder is trained to map the behavior of subjects into a low-dimensional latent space. These low-dimensional representations are used to generate the parameters of individual RNNs corresponding to the decision-making process of each subject. We introduce terms into the loss function that ensure that the latent dimensions are informative and disentangled, i.e., encouraged to have distinct effects on behavior. This allows them to align with separate facets of individual differences. We illustrate the performance of our framework on synthetic data as well as a dataset including the behavior of patients with psychiatric disorders.}

{Individuals differ in how they learn from experience. In Pavlovian conditioning models, where cues predict reinforcer delivery at a different goal location, some animals-called sign-trackers-come to approach the cue, whereas others, called goal-trackers, approach the goal. In sign-trackers, model-free phasic dopaminergic reward-prediction errors underlie learning, which renders stimuli \textquotesinglewanted\textquotesingle. Goal-trackers do not rely on dopamine for learning and are thought to use model-based learning. We demonstrate this double dissociation in 129 male humans using eye-tracking, pupillometry and functional magnetic resonance imaging informed by computational models of sign- and goal-tracking. We show that sign-trackers exhibit a neural reward prediction error signal that is not detectable in goal-trackers. Model-free value only guides gaze and pupil dilation in sign-trackers. Goal-trackers instead exhibit a stronger model-based neural state prediction error signal. This model-based construct determines gaze and pupil dilation more in goal-trackers.}

{The noradrenergic locus coeruleus (LC) is crucial for controlling brain and behavioral states. While synchronous stimulation of LC neurons evokes a single activated cortical state with increased high-frequency power, little is known about how spontaneous patterns of LC population activity drive cortical states. Since LC neurons selectively project to specific forebrain regions, we hypothesized that individual LC ensembles produce different cortical states. We recorded up to 34 single units simultaneously in the rat LC and used non-negative matrix factorization to identify spontaneously activated ensembles of co-active LC neurons. The ensembles were active mostly at different times and were simultaneously active only rarely. We assessed cortical state in area 24a by examining local field potential power spectrograms triggered on activations of individual LC ensembles. We observed four spectrotemporally-distinct cortical states associated with activation of specific LC ensembles. Thus, distinct spontaneously active LC ensembles contribute to unexpectedly diverse cortical states.}

{When knowledge is scarce, it is adaptive to seek further information to resolve uncertainty and obtain a more accurate worldview. Biases in such information-seeking behavior can contribute to the maintenance of inaccurate views. Here, we investigate whether predispositions for uncertainty-guided information seeking relate to individual differences in dogmatism, a phenomenon linked to entrenched beliefs in political, scientific, and religious discourse. We addressed this question in a perceptual decision-making task, allowing us to rule out motivational factors and isolate the role of uncertainty. In two independent general population samples (n \textequals 370 and n \textequals 364), we show that more dogmatic participants are less likely to seek out new information to refine an initial perceptual decision, leading to a reduction in overall belief accuracy despite similar initial decision performance. Trial-by-trial modeling revealed that dogmatic participants placed less reliance on internal signals of uncertainty (confidence) to guide information search, rendering them less likely to seek additional information to update beliefs derived from weak or uncertain initial evidence. Together, our results highlight a cognitive mechanism that may contribute to the formation of dogmatic worldviews.}

{PURPOSE: To develop a robust design of a human head double-tuned 31 P/1 H array, which provides good performance at both 31 P and 1 H frequencies for MR spectroscopic imaging at 9.4T. METHODS: Increasing the number of surface loops in a human head array improves the peripheral signal-to-noise ratio (SNR), while the central SNR doesn\textquotesinglet substantially change. High peripheral SNR can contaminate MR spectroscopic imaging data at both 1 H and 31 P frequency. To minimize this effect, we limited the number of elements in the 31 P array to 10, i.e., 8 transceiver surface loops circumscribing the head and 2 receive "vertical" loops placed at the superior location. The 1 H-portion of the array also consists of 10 elements, i.e., 8 transceiver surface loops circumscribing the head and 2 transceiver "vertical" loops at the superior location of the head. Both the 31 P array and 1 H array are placed in a single layer at the same distance to the head, which provides high loading and, thus, a good performance for both arrays. RESULTS: Transmit efficiency of the 1 H-portion of the double-tuned array was very similar to that of the single-tuned arrays of similar size. Also, addition of the cross-loops substantially improved the brain coverage. CONCLUSION: We developed a novel 31 P/1 H double-tuned array for MR spectroscopic imaging of a human brain at 9.4T. Placing both 31 P and 1 H loops in a single layer provides for high transmit efficiency at both frequencies without compromising SNR near the brain center at the 31 P-frequency. Addition of the cross-loops at the superior location improves the brain coverage.}

{PURPOSE: We assessed how improved static magnetic field (B0 ) homogeneity with a dynamic multicoil shimming can influence the blood oxygen level dependent (BOLD) contrast to noise when echo planar imaging (EPI) sequence is used for a motor task functional MRI study. We showed that a multicoil shim setup can be a proper choice for dynamic shimming of 2 spatially distant areas with different inhomogeneity distributions. METHODS: A 16-channel multicoil shim setup is used to provide improved B0 homogeneity by dynamic slice-wise shimming. The performance of dynamic B0 shimming was investigated in 2 distinct brain regions, the motor cortex and the cerebellum, in the same experiment during a finger-tapping task. Temporal SNR (tSNR), geometric distortion of the EPIs, and results of an analysis with a general linear model before and after shimming with the multicoil were compared. RESULTS: Reduced B0 deviation by 30\textpercent and 52\textpercent in the cerebellum and motor cortex, respectively, resulted in higher tSNR and a reduction of distortions in the EPI. Statistical analysis applied to the EPIs showed higher t values and increased number of voxels above significance threshold when shimming with the multicoil setup. CONCLUSIONS: Improved B0 homogeneity leads to higher tSNR and enhances the detection of BOLD signal.}

{Remarkable progress has come from whole-brain models linking anatomy and function. Paradoxically, it is not clear how a neuronal dynamical system running in the fixed human anatomical connectome can give rise to the rich changes in the functional repertoire associated with human brain function, which is impossible to explain through long-term plasticity. Neuromodulation evolved to allow for such flexibility by dynamically updating the effectivity of the fixed anatomical connectivity. Here, we introduce a theoretical framework modeling the dynamical mutual coupling between the neuronal and neurotransmitter systems. We demonstrate that this framework is crucial to advance our understanding of whole-brain dynamics by bidirectional coupling of the two systems through combining multimodal neuroimaging data (diffusion magnetic resonance imaging [dMRI], functional magnetic resonance imaging [fMRI], and positron electron tomography [PET]) to explain the functional effects of specific serotoninergic receptor (5-HT2AR) stimulation with psilocybin in healthy humans. This advance provides an understanding of why psilocybin is showing considerable promise as a therapeutic intervention for neuropsychiatric disorders including depression, anxiety, and addiction. Overall, these insights demonstrate that the whole-brain mutual coupling between the neuronal and the neurotransmission systems is essential for understanding the remarkable flexibility of human brain function despite having to rely on fixed anatomical connectivity.}

{Simultaneous EEG-fMRI has been attracting significant attention from neuroscientists for the last 20 years. During these years, EEG-fMRI has been in use to investigate brain at different field strengths. However, so far, there has been no study examining the potential use of EEG during MRI at 9.4T. Therefore, in this study, we used a customized EEG and acquired data with RF heating, gradient heating, B0, B1, and fMRI using a phantom. The results revealed no critical increase in temperature in the EEG. However, there is an observable decrease in B1, which indicates the need for further research in this direction.}

{Trust is central to bonding and cooperation. In many social interactions, individuals need to trust another person exclusively on the basis of their subjective impressions of the other\textquotesingles trustworthiness. Such impressions can be formed from social information from faces (e.g., facial trustworthiness and attractiveness) and guide trusting behaviors via activations of dopaminergic brain regions. However, the specific dopaminergic effects on impression-based trust are to date elusive. Here, in a double-blind, placebo-controlled, within-subject design, we administrated a D2/D3 dopamine agonist (pramipexole) to 28 healthy females who subsequently played a one-shot trust game with partners of varying facial trustworthiness. Our results show that by minimizing facial attractiveness information, we could isolate the specific effects of facial trustworthiness on trust in unknown partners. Despite no modulation of trustworthiness impressions, pramipexole intake significantly impacted trusting behaviors. Notably, these effects of pramipexole on trusting behaviors interacted with participants\textquotesingle hormonal contraceptive use. In particular, after pramipexole intake, trust significantly decreased in hormonal contraceptive non-users. This study fills an important gap in the experimental literature on trust and its neural dynamics, unearthing the cognitive and neural modulations of trusting behaviors based on trustworthiness impressions of others.}

{A variety of conceptualizations of psychological uncertainty exist. From an information-theoretic perspective, probabilistic uncertainty can be formalized as mathematical entropy. Cognitive emotion theories posit that uncertainty appraisals and motivation to reduce uncertainty are modulated by emotional state. Yet little is known about how people evaluate probabilistic uncertainty, and about how emotional state modulates people\textquoterights evaluations of probabilistic uncertainty and behavior to reduce probabilistic uncertainty. We tested intuitive entropy evaluations and entropy reduction strategies across four emotion conditions in the Entropy Mastermind game. We used the unified Sharma-Mittal space of entropy measures to quantify participants\textquoteright entropy evaluations. Results suggest that many people use a heuristic strategy, focusing on the number of possible outcomes, irrespective of the probabilities in the probability distribution. This result is surprising, given that previous work suggested that people are very sensitive to the maximum probability when choosing queries on probabilistic classification tasks. Emotion induction generally increased participants\textquoteright heuristic assessment. The uncertainty associated with emotional states also affected game play: participants needed fewer queries and spent less time on games in high-uncertainty than in low-uncertainty emotional states. Yet entropy perceptions were not related to subjectively reported uncertainty, numeracy or entropy knowledge, suggesting that entropy perceptions may form an independent psychological construct.}

{In this work, we introduce new phenomenological neuronal models (eLIF and mAdExp) that account for energy supply and demand in the cell as well as their interactions with spiking dynamics. Through energetic considerations, these new models reproduce a broad range of biologically-relevant behaviors that are identified to be crucial in many neurological disorders, but were not captured by commonly used phenomenological models. Because of their low dimensionality eLIF and mAdExp enable large-scale simulations that are necessary for more realistic studies of brain circuits involved in neuronal disorders. The new models enable both more accurate modeling and the possibility to study energy-associated disorders over the whole time-course of disease progression instead of only comparing the initially healthy status with the final diseased state. These models, therefore, provide new theoretical and computational methods to assess the opportunities of early diagnostics and the potential of energy-centered approaches to improve therapies.}

{Timescales characterize the pace of change for many dynamic processes in nature: radioactive decay, metabolization of substances, memory decay in neural systems, and epidemic spreads. Measuring timescales from experimental data can reveal underlying mechanisms and constrain theoretical models. Timescales are usually estimated by fitting the autocorrelation of sample time-series with exponential decay functions. We show that this standard procedure often fails to recover the correct timescales, exhibiting large estimation errors due to a statistical bias in autocorrelations of finite data samples. To overcome this bias, we develop a method using adaptive Approximate Bayesian Computations. Our method estimates the timescales by fitting the autocorrelation of sample data with a generative model based on a mixture of Ornstein-Uhlenbeck processes. The method accounts for finite sample size and noise in data and returns a posterior distribution of timescales quantifying the estimation uncertainty. We demonstrate how the posterior distribution can be used for model selection to compare alternative hypotheses about the dynamics of the underlying process. Our method accurately recovers the correct timescales on synthetic data from various processes with known ground truth dynamics. We illustrate its application to electrophysiological recordings from the primate cortex.}

We propose a nonparametric parameter estimation of confidence intervals when the underlying has large or infinite variance. We explain the method by a simple numerical example and provide an application to estimate the coupling strength in neuronal networks.

{Invited for this month\textquotesingles cover are the collaborating groups of Dr. Goran Angelovski from the Max Planck Institute for Biological Cybernetics in Tuebingen, Germany and Prof. Carlos Platas-Iglesias from Centro de Investigaci\'ons Cient\'\ificas Avanzadas (CICA) and Departamento de Qu\'\imica, Facultade de Ciencias, Universidade da Coru\~na, A Coru\~na, Spain. The cover image shows chameleon-like molecules whose luminescence emission can be enhanced selectively and strongly by the coordination of zinc(II) ions. Read the full text of the article at 10.1002/cplu.201900731.}

{Zinc ions play an important role in many biological processes in the human body. To selectively detect Zn2+, two EuDO3A-based complexes (DO3A\textequals1,4,7,10-tetraazacyclododecane-1,4,7-tricarboxylic acid) appended with tyrosine as a chromophore and di-(2-picolyl)amine (DPA) as the Zn2+ recognition moiety were developed as suitable luminescent sensors. Their luminescence intensity is affected by the photoinduced electron transfer mechanism. Upon addition of Zn2+, both probes display an up to sevenfold enhancement in Eu3+ emission. Competition experiments demonstrated their specificity toward Zn2+ over other metal ions, while also revealing the nonspecificity of the derivatives lacking the DPA-moiety, thus confirming the essential role of the DPA for the recognition of Zn2+. The induced emission changes of Eu3+ allow for precise quantitative analysis of Zn2+, establishing these lanthanide-based complexes as viable chemosensors for biological applications.}

{The colors that people see depend not only on the surface properties of objects but also on how these properties interact with light as well as on how light reflected from objects interacts with an individual\textquoterights visual system. Because individual visual systems vary, the same visual stimulus may elicit different perceptions from different individuals. \#thedress phenomenon drove home this point: different individuals viewed the same image and reported it to be widely different colors: blue and black versus white and gold. This phenomenon inspired a collection of demonstrations presented at the Vision Sciences Society 2015 Meeting which showed how spatial and temporal manipulations of light spectra affect people\textquoterights perceptions of material colors and illustrated the variability in individual color perception. The demonstrations also explored the effects of temporal alterations in metameric lights, including Maxwell\textquoterights Spot, an entoptic phenomenon. Crucially, the demonstrations established that \#thedress phenomenon occurs not only for images of the dress but also for the real dress under real light sources of different spectral composition and spatial configurations.}

{The primary visual cortex of humans contains patches of neurons responding preferentially to stimulation of one eye (the ocular dominance columns). Multiple previous studies attempted to detect their activity using fMRI. The majority of these fMRI studies used magnetic field strengths of 4 ​T and higher. However, there have been reports of reliable eye-selective activations at 3 ​T as well. In this study we investigated the possibility of detecting eye-selective V1 activity using high-resolution GE-EPI fMRI at 3 ​T and sub-millimeter resolution fMRI at ultrahigh 9.4 ​T magnetic field strengths with acquisition parameters optimized for each field strength. High-resolution fMRI at 9.4 ​T also allowed us to examine the eye-selectivity responses across the cortical depth, which are expected to be strongest in the middle layers. We observed a substantial increase in the percentage of eye-selective voxels, as well as a doubling in run-to-run consistency of eye preference at ultrahigh field compared to 3 ​T. We also found that across cortical depth, eye selectivity increased towards the superficial layers, and that signal contrast increased while noise remained nearly constant towards the surface. The depth-resolved results are consistent with a distortion of spatial specificity of the GE-EPI signal by ascending venules and large draining veins on the cortical surface. The effects of larger vessels cause increasing signal amplitude, but also displacement of the maximum BOLD signal relative to neural activity. In summary, our results show that increase in spatial resolution, reduced partial volume effects, and improved sensitivity at 9.4 ​T allow for better detection of eye-selective signals related to ocular dominance columns. However, although ultrahigh field yields higher sensitivity to the ocular dominance signal, GE-EPI still suffers from specificity issues, with a prominent signal contribution at shallow depths from larger cortical vessels.}

{Metacognition, the ability to internally evaluate our own decisions, is particularly useful since many real-life decisions lack immediate feedback. Most previous studies have focused on the construction of confidence at the level of single decisions, but little is known about the formation of \textquotedblleftglobal\textquotedblright self-performance estimates (SPEs) aggregated from multiple decisions. Here, we compare the formation of SPEs in the presence and absence of feedback, testing the hypothesis that local decision confidence supports the formation of SPEs when feedback is unavailable. In a series of three behavioral experiments (N\textequals29, N\textequals29 and N\textequals46 subjects), we reveal that humans pervasively underestimate their performance in the absence of feedback, compared to a condition with full feedback, despite objective performance being unaffected. We found that fluctuations in confidence contribute to global SPEs over and above objective accuracy and reaction times. Preliminary neuroimaging results (fMRI) suggest that during SPE formation, prefrontal areas may differently represent local confidence signals according to whether they are congruent or incongruent with global SPEs (N\textequals39 subjects). Our findings create a bridge between local confidence and global SPEs, and support a functional role for confidence in higher-order behavioral control.}

{The brainstem engages in various brain functions. However, we still lack a detailed understanding of its underlying functional organization. Therefore, in this study, we analyzed 222300 fMRI scans of 62 subjects acquired at the HCP-7 Tesla project. We applied the instantaneous parcellation analysis (ICP) method and determined a reliable, reproducible, and stable functional anatomy of the brainstem. The results reveal that stable multi-scale functional anatomy exists in the brainstem. The spatial rich functional anatomy enables neuroscientists to better characterize brainstem organization and understand its function and role in various brain disorders.}

{Accurately segmenting MRI images is crucial for many cli-nical applications. However, manually segmenting imageswith accurate pixel precision is a tedious and time consu-ming task. In this paper we present a simple, yet effectivemethod to improve the efficiency of the image segmenta-tion process. We propose to transform the image annota-tion task into a binary choice task. We start by using classi-cal image processing algorithms with different parametervalues to generate multiple, different segmentation masksfor each input MRI image. Then, instead of segmenting thepixels of the images, the user only needs to decide whethera segmentation is acceptable or not. This method allowsus to efficiently obtain high quality segmentations with mi-nor human intervention. With the selected segmentations,we train a state-of-the-art neural network model. For theevaluation, we use a second MRI dataset (1.5T Dataset),acquired with a different protocol and containing annota-tions. We show that the trained network i) is able to au-tomatically segment cases where none of the classical me-thods obtain a high quality result ; ii) generalizes to thesecond MRI dataset, which was acquired with a differentprotocol and was never seen at training time ; and iii) en-ables detection of miss-annotations in this second dataset.Quantitatively, the trained network obtains very good re-sults : DICE score - mean 0.98, median 0.99- and Haus-dorff distance (in pixels) - mean 4.7, median 2.0-.}

{Haptic support systems have been widely used for supporting human operators when performing a manual control task. These systems are commonly designed to track known target trajectories. However, the trajectory to track is not known in many realistic cases. For instance, the pilot-intended trajectory is not known beforehand when considering a helicopter pilot flying in free-flight. This paper proposes a possible approach to design a haptic shared control system when the target trajectory is not known a priori. Especially, the aim of the proposed design is to help minimally trained pilots during a 2-degree-of-freedom lateral/longitudinal helicopter free-flight task. To accomplish this goal, first, a Pilot Intent Estimator (PIE) is developed to infer pilot intent. Then, the corresponding intended trajectory is generated. Finally, a haptic feedback is designed to track the estimated intended trajectory. The designed PIE was evaluated in a preliminary test with an experienced helicopter pilot. Then, a human-in-the-loop experiment with minimally trained participants was conducted to assess the proposed shared control system. Results showed the effectiveness of the PIE to estimate the correct direction of motion chosen by the pilot. Furthermore, the haptic feedback helped participants to accomplish the control task with better task performance (i.e., lower tracking errors and lower amount of control activity) compared with manual control.}

{Numerous researchers have put forward heuristics as models of human decision making. However, where such heuristics come from is still a topic of ongoing debates. In this work we propose a novel computational model that advances our understanding of heuristic decision making by explaining how different heuristics are discovered and how they are selected. This model, called bounded meta-learned inference, is based on the idea that people make environment-specific inferences about which strategies to use, while being efficient in terms of how they use computational resources. We show that our approach discovers two previously suggested types of heuristics -- one reason decision making and equal weighting -- in specific environments. Furthermore, the model provides clear and precise predictions about when each heuristic should be applied: knowing the correct ranking of attributes leads to one reason decision making, knowing the directions of the attributes leads to equal weighting, and not knowing about either leads to strategies that use weighted combinations of multiple attributes. This allows us to gain new insights on mixed results of prior empirical work on heuristic decision making. In three empirical paired comparison studies with continuous features, we verify predictions of our theory, and show that it captures several characteristics of human decision making not explained by alternative theories.}

{Four albumin-nitroxide conjugates were prepared and tested as metal-free organic radical contrast agents (ORCAs) for magnetic resonance imaging (MRI). Each human serum albumin (HSA) carrier bears multiple nitroxides conjugated via homocysteine thiolactones. These molecular conjugates retain important physical and biological properties of their HSA component, and the resistance of their nitroxide groups to bioreduction was retained or enhanced. The relaxivities are similar for these four conjugates and are much greater than those of their individual components: the HSA or the small nitroxide molecules. This new family of conjugates has excellent prospects for optimization as ORCAs}

{Background: Quantitative assessment of pulmonary blood flow and visualization of its temporal and spatial distribution without contrast media is of clinical significance. Purpose: To assess the potential of electrocardiogram (ECG)-triggered pseudocontinuous arterial spin labeling (PCASL) imaging with balanced steady-state free-precession (bSSFP) readout to measure lung perfusion under free-breathing (FB) conditions and to study temporal and spatial characteristics of pulmonary blood flow. Study type: Prospective, observational. Subjects: Fourteen volunteers; three patients with pulmonary embolism. Field strength/sequences: 1.5T, PCASL-bSSFP. Assessment: The pulmonary trunk was labeled during systole. The following examinations were performed: 1) FB and timed breath-hold (TBH) examinations with a postlabeling delay (PLD) of 1000 msec, and 2) TBH examinations with multiple PLDs (100-1500 msec). Scan-rescan measurements were performed in four volunteers and one patient. Images were registered and the perfusion was evaluated in large vessels, small vessels, and parenchyma. Mean structural similarity indices (MSSIM) was computed and time-to-peak (TTP) of parenchymal perfusion in multiple PLDs was evaluated. Image quality reading was performed with three independent blinded readers. Statistical tests: Wilcoxon test to compare MSSIM, perfusion, and Likert scores. Spearman\textquotesingles correlation to correlate TTP and cardiac cycle duration. The repeatability coefficient (RC) and within-subject coefficient of variation (wCV) for scan-rescan measurements. Intraclass correlation coefficient (ICC) for interreader agreement. Results: Image registration resulted in a significant (P \textless 0.05) increase of MSSIM. FB perfusion values were 6\textpercent higher than TBH (3.28 $\pm$ 1.09 vs. 3.10 $\pm$ 0.99 mL/min/mL). TTP was highly correlated with individuals\textquotesingle cardiac cycle duration (Spearman \textequals 0.89, P \textless 0.001). RC and wCV were better for TBH than FB (0.13-0.19 vs. 0.47-1.54 mL/min/mL; 6-7 vs. 19-60\textpercent). Image quality was rated very good, with ICCs 0.71-0.89. Data conclusion: ECG-triggered PCASL-bSSFP imaging of the lung at 1.5T can provide very good image quality and quantitative perfusion maps even under FB. The course of labeled blood through the lung shows a strong dependence on the individuals\textquotesingle cardiac cycle duration.}