Publications

{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.}

{Functional imaging with sub-millimeter spatial resolution is a basic requirement for assessing functional MRI (fMRI) responses across different cortical depths, and is used extensively in the emerging field of laminar fMRI. Such studies seek to investigate the detailed functional organization of the brain and may develop to a new powerful tool for human neuroscience. However, several studies have shown that measurement of laminar fMRI responses can be biased by the image acquisition and data processing strategies. In this work, measurements with three different gradient-echo EPI protocols with a voxel size down to 650 $\mu$m isotropic were performed at 9.4 T. We estimated how prospective motion correction can help to improve spatial accuracy by reducing the number of spatial resampling steps in postprocessing. In addition, we demonstrate key requirements for accurate geometric distortion correction to ensure that distortion correction maps are properly aligned to the functional data and that strong variations of distortions near large veins can lead to signal overlays which cannot be corrected for during postprocessing. Furthermore, this study illustrates the spatial extent of bias induced by pial and other larger veins in laminar BOLD experiments. Since these issues under investigation affect studies performed with more conventional spatial resolutions, the methods applied in this work may also help to improve the understanding of the BOLD signal more broadly.}

{Cocaine addiction is characterized by overwhelming craving for the substance, which drives its escalating use despite adverse consequences. Animal models suggest a disrupted glutamate homeostasis in the nucleus accumbens to underlie addiction-like behavior. After chronic administration of cocaine, rodents show decreased levels of accumbal glutamate, whereas drug-seeking reinstatement is associated with enhanced glutamatergic transmission. However, due to technical obstacles, the role of disturbed glutamate homeostasis for cocaine addiction in humans remains only partially understood, and accordingly, no approved pharmacotherapy exists. Here, we applied a tailored proton magnetic resonance spectroscopy protocol that allows glutamate quantification within the human nucleus accumbens. We found significantly reduced basal glutamate concentrations in the nucleus accumbens in cocaine-addicted (N \textequals 26) compared with healthy individuals (N \textequals 30), and increased glutamate levels during cue-induced craving in cocaine-addicted individuals compared with baseline. These glutamatergic alterations, however, could not be significantly modulated by a short-term challenge of N-acetylcysteine (2400 mg/day on 2 days). Taken together, our findings reveal a disturbed accumbal glutamate homeostasis as a key neurometabolic feature of cocaine addiction also in humans. Therefore, we suggest the glutamatergic system as a promising target for the development of novel pharmacotherapies, and in addition, as a potential biomarker for a personalized medicine approach in addiction.}

{Using a contingency volatility manipulation, we tested the hypothesis that difficulty adapting probabilistic decision-making to second-order uncertainty might reflect a core deficit that cuts across anxiety and depression and holds regardless of whether outcomes are aversive or involve reward gain or loss. We used bifactor modeling of internalizing symptoms to separate symptom variance common to both anxiety and depression from that unique to each. Across two experiments, we modeled performance on a probabilistic decision-making under volatility task using a hierarchical Bayesian framework. Elevated scores on the common internalizing factor, with high loadings across anxiety and depression items, were linked to impoverished adjustment of learning to volatility regardless of whether outcomes involved reward gain, electrical stimulation, or reward loss. In particular, high common factor scores were linked to dampened learning following better-than-expected outcomes in volatile environments. No such relationships were observed for anxiety- or depression-specific symptom factors.}

{It has been shown that neural networks combined with variable k-space undersampling (MultiNet GRAPPA) is superior to a conventional GRAPPA reconstruction at 9.4T. Here, the feasibility of performing MultiNet GRAPPA for 1H FID-MRSI at 7T is investigated with and without novel modifications to the original acquisition/reconstruction scheme. In this study, it is shown that MultiNet GRAPPA is shown to be feasible for 1H MRSI acceleration at 7T with a new k-space undersampling scheme for higher signal-to-noise and increased map reliability and use of a novel technique to increase SNR retention using semi-synthetic calibration data without an increase in acquisition time.}

{The double-echo steady-state (DESS) sequence has been used successfully in 3T MRI imaging of the musculoskeletal system for segmentation of joint. However, in 3D-DESS images acquired at 7T MRI, a reduction in the contrast between tissues due to an increased diffusion sensitivity may complicate cartilage segmentation. Typically, the signals acquired with DESS are averaged without any pre-processing. However, these signals give different contrasts and have different noise behaviours. In this work, we improve the contrast-to-noise ratio (CNR) whilst preserving anatomical detail in high-resolution 7T DESS images through a new approach combining L1-norm denoising and p-norm combination of the echo signals.}

{We report on a macrocyclic platform based on an 18-membered macrocycle that forms kinetically highly inert paramagnetic complexes and possesses an excellent outlook for the development of bioresponsive paraCEST (paramagnetic chemical exchange saturation transfer) contrast agents. The investigated europium(III) chelate is non-hydrated and contains four amide groups, each possessing two paramagnetically shifted proton resonances distant from bulk water. The X-ray crystal structure and solution studies indicate that the metal ion is ten-coordinated, being directly bound to the six N atoms of the macrocycle and the four amide O atoms of the pendant arms. The complex presents an excellent inertness with respect to dissociation, being stable under a variety of harsh conditions, including highly acidic and basic media or elevated temperatures. The amide protons are in slow-to-intermediate exchange with bulk water, which gives rise to the generation of a strong CEST effect at low probe concentration and saturation powers ($\sim$25\textpercent at 5 mM, B1 \textequals 5 $\mu$T, 37 \mbox{$^\circ$}C). We demonstrate the potential of this platform for mapping pH in its microenvironment and foresee potential for the development of diverse paraCEST probes and sensors.}

{Introduction: In functional MRI (fMRI) echo planar imaging (EPI) is often combined with parallel imaging, e.g. GRAPPA (1), to increase temporal resolution. The auto-calibration scans (ACS) required for the calculation of the coil sensitivities in the parallel imaging reconstruction are conventionally acquired in a segmented fashion (number of segments \textequals parallel imaging factor), with the individual segments of each slice separated by the repetition time (TR). However, in case of TRs in the range of several seconds, ACS segments may be acquired at different B0-field offsets e.g. due to respiration or motion. These fluctuations can result in variations in temporal SNR (tSNR) across different slices particularly at high-field (3). The sensitivity of tSNR on physiological effects can be reduced by acquiring all segments of a slice successively with minimum delay in the so called FLEET technique (3). Alternatively, a FLASH readout, which is more robust against B0-field changes, can be used to obtain the ACS data (2). Although physiological influences are usually considered to be the main cause of tSNR variations at long TRs, as far as we know, the performance of various GRAPPA pre-scan methods (conventional, FLEET and FLASH) has not previously been investigated for a TR in the sub-second range. Methods: Four healthy subjects were measured at 9.4 Tesla (Siemens Healthineers, Germany) using an in-house-built 16Tx-31Rx head-coil (4). Gradient-echo EPIs were acquired for two regions covering a major part of the thalamus (ROI 1) and the motor cortex (ROI 2). Imaging parameters: TE/TR \textequals 23/600ms, FA \textequals 40\mbox{$^\circ$}, 12 slices, 150 volumes. Two different spatial resolutions were used: \textbullet 1 x 1 x 2 mm\mbox{$^3$}: mtx \textequals 192x192, 6/8 partial Fourier, GRAPPA \textequals 4 (60 ACS lines), echo spacing \textequals 1.01 ms. \textbullet 2 x 2 x 2 mm\mbox{$^3$}: mtx \textequals 96x96, GRAPPA \textequals 3 (45 ACS lines), echo spacing \textequals 0.8 ms. The two protocols were repeated for both ROIs for all three ACS sampling methods: conventional, FLEET, and FLASH. The excitation flip angle for the FLEET and FLASH ACS scans was 10\mbox{$^\circ$} and 15\mbox{$^\circ$}, respectively. Temporal SNR maps were calculated as the mean signal value across time divided by its temporal standard deviation. To quantify the tSNR for the different GRAPPA pre-scan methods, mean tSNR values were assessed for each ROI after performing manual brain masking. Results: Figure 1 shows the calculated tSNR for the different GRAPPA pre-scan methods and brain regions in an example volunteer. The lowest tSNR is visible for the data measured with conventional ACS and low spatial resolution in particular. This observation is consistent for both ROIs. Averaged over all slices, the tSNR values in images acquired with FLEET or FLASH ACS sampling are higher than with conventional ordering, too (Figure 2). This is especially the case at low image resolution. At high spatial resolution, the tSNR of data reconstructed using FLEET and FLASH sampled data is almost identical and the improvement compared to the conventional method is rather small ($\sim$12\textpercent in ROI 1 and $\sim$25\textpercent in ROI 2). Conclusions: Although physiological influences and respiration effects in particular are expected to be reduced for sub-second TR, the FLEET and FLASH pre-scan methods yielded clearly higher tSNR compared to the conventional approach. One explanation is, that despite the short TR, the acquisition of all ACS lines still took about 1.8 s (2x2x2 mm\mbox{$^3$}) and 2.4 s (1x1x2 mm\mbox{$^3$}), respectively, due to the slice-segment acquisition scheme, whereas the FLEET method only required about 200 ms (1x1x2 mm\mbox{$^3$}). This study also confirmed that the impact of physiological fluctuations on tSNR heavily scales with the spatial resolution, as it is the case for un-accelerated imaging (5). Thus, even though less tSNR improvement can be expected for alternative ACS acquisition techniques at high spatial resolutions, it still has to be considered as a potential source for effect size differences even in sub-second TR fMRI studies.}

{Post-mortem brain MRI can yield valuable information. However, tissue preservation and MR-compatibility of fixation agents are challenging. Here we investigated the effect of four MR-compatible formalin-based fixatives on the MR properties of pig brains at several timepoints after start of fixation up to one month. The inclusion agents known to improve the dielectric properties of the fixatives lead to greater R2\textasteriskcentered difference between GM-WM than conventional fixatives. Vice versa these agents lead to a decrease in T1 contrast.}

{The brain has persistent internal states that can modulate every aspect of an animal\textquoterights mental experience1,2,3,4. In complex tasks such as foraging, the internal state is dynamic5,6,7,8. Caenorhabditis elegans alternate between local search and global dispersal5. Rodents and primates exhibit trade-offs between exploitation and exploration6,7. However, fundamental questions remain about how persistent states are maintained in the brain, which upstream networks drive state transitions and how state-encoding neurons exert neuromodulatory effects on sensory perception and decision-making to govern appropriate behaviour. Here, using tracking microscopy to monitor whole-brain neuronal activity at cellular resolution in freely moving zebrafish larvae9, we show that zebrafish spontaneously alternate between two persistent internal states during foraging for live prey (Paramecia). In the exploitation state, the animal inhibits locomotion and promotes hunting, generating small, localized trajectories. In the exploration state, the animal promotes locomotion and suppresses hunting, generating long-ranging trajectories that enhance spatial dispersion. We uncover a dorsal raphe subpopulation with persistent activity that robustly encodes the exploitation state. The exploitation-state-encoding neurons, together with a multimodal trigger network that is associated with state transitions, form a stochastically activated nonlinear dynamical system. The activity of this oscillatory network correlates with a global retuning of sensorimotor transformations during foraging that leads to marked changes in both the motivation to hunt for prey and the accuracy of motor sequences during hunting. This work reveals an important hidden variable that shapes the temporal structure of motivation and decision-making.}

{There is growing evidence that obesity is associated with inflammation in the brain, which could contribute to the pathogenesis of obesity. In humans, it is challenging to detect brain inflammation in vivo. Recently, quantitative magnetic resonance imaging (qMRI) has emerged as a tool to characterize pathophysiological processes in the brain with reliable and reproducible measures. Proton density imaging provides quantitative assessment of the brain water content, which is affected in different pathologies including inflammation. We enrolled 115 normal weight, overweight and obese men and women (body mass index (BMI) range 20.1-39.7 kg/m2, age range 20-75 years, 60\textpercent men) to acquire cerebral water content mapping in vivo using MRI at 3 Tesla. We investigated potential associations between brain water content with anthropometric measures of obesity, body fat distribution and whole-body metabolism. No global changes in water content were associated with obesity. However, higher water content values in the cerebellum, limbic lobe and sub-lobular region was detected in participants with higher BMI, independent of age. More specifically, the dorsal striatum, hypothalamus, thalamus, fornix, anterior limb of the internal capsule and posterior thalamic radiation showed the strongest relationship with BMI, independent of age. In a subgroup with available measurements (n\textequals50), we identified visceral adipose tissue to be the strongest tested link between higher water content values and obesity. Persons with metabolic syndrome had the highest water content values in the hypothalamus and the fornix. There is accumulating evidence that inflammation of the hypothalamus contributed to obesity-associated insulin resistance in that area. Whether brain inflammation is a cause or consequence of obesity in humans still needs to be investigated using a longitudinal study design. Using qMRI, we were able to detect marked water content changes in young and older obese adults, which is most likely due to chronic low-grade inflammation.}

{Many processes in the brain require a constant supply of energy. Notably, spiking and maintenance of ion concentration gradients by the Na/K pump require active mechanisms and consumes energy in the form of ATP. Though the brain has a safety margin on energy production compared to the consumption during maximum activity, this margin is small and energy stores can be quickly exhausted, for instance in the context of neuronal disorders [1]. Once energy becomes insufficient, neuronal response can change drastically, leading to intermittent spikes, bursts, network oscillations, or seizures [2]. This crucial impact of metabolic disruption is not addressed in any of the standard neuronal models used in computational neuroscience. We fill this significant gap in current modeling frameworks, by introducing two novel model-neurons that account for energetic constraints while remaining computationally efficient, analytically tractable, and biologically interpretable. Using these new types of neurons, we can reproduce both crucial single-cell behaviors, such as depolarization blocks or bistability, and complex changes in collective dynamics that were not capture by previous integrate-and-fire neurons (shown on panels A and B of the figure). In this presentation, we will discuss the main mechanisms that define the models\textquoteright equations: the role of pumps that degrade ATP into ADP to maintain ion gradients (most notably the Na/K and calcium pumps) and ATP-gated potassium (K-ATP) channels, which open or close depending on the ATP/ADP ratio. We explain how these mechanisms shape the relationship between energy availability and neuronal excitability [3-4] and illustrate some of the potential consequences. In particular we discuss the influence of metabolic disruption on the information processing capabilities of neurons (see panel A for an example of disease progression on a network in the well-known AI state). These novel models enable for the first time the theoretical study of energy-associated disorders over the whole time-course of disease progression, instead of only comparing the initially healthy condition with the final diseased state. This advancement is essential to model multiple neurological disorders, such as epilepsy or Parkinson\textquoterights disease, as it enables theoretical and computational studies to assess the opportunities of early diagnostics and the potential of energy-centered approaches to improve therapies.}

{In the following report, using a model adapted to real-world data of the spread of COVID-19, it is shown that if COVID-19 is spread in Iran without any containment policy, which is more or less the case at the moment of writing this text, it can lead to disastrous consequences. Although the reports show that the current situation in Iran is already very bad, simulations show that the worse is yet to come. The peak number of infected people in Iran will probably happen in 1.5-2 months from now. Simulations show that in an uncontained human population with the age structure of the Iranian population, at the peak of the infection, around 11\textpercent of the population will be infected simultaneously. And eventually, 65\textpercent of the population will be infected. For a country as big as Iran, these peak values might reach in different days in different cities or regions. But all are expected to reach these peak values days or weeks apart. If we assume only 5\textpercent of those infected with COVID-19 need respiratory or intensive care, the total number of hospital beds needed at the peak of the epidemic in Iran is around 467, 000 beds. Also, in the uncontained scenario, eventually 65\textpercent of the population will be infected with COVID-19 in a matter of months, which is around 55 million people for a country of around 85 million inhabitants. Even with a mortality rate of 0.2\textpercent, which admittedly is optimistic for such a huge number of potential patients, total death would amount to around 110, 000 people. To avoid such an unprecedented disaster, there is an immediate need to implement effective containment strategies. I have presented a detailed quantitative analysis of the effectiveness of various containment policies. The most effective, among the ones humanly possible, is a total lock-down of the population, closing down all governmental offices, companies, banks and schools and forcing people to stay home. A policy which is already enforced by governments in different countries around the world. Simulations show that if such a policy is imposed today, it can bring down the total number of infected to around 7\textpercent of the population, down from 65\textpercent. But even in such a case, considering the current level of the spread of the COVID-19 virus in the Iranian population, eradicating it would take months. It needs patience and determination and a long-term strategy to manage the number of infected people in the population until the vaccine for COVID-19 is publicly available.}

{In this paper, we investigate the principle that \textasciigravegood explanations are hard to vary\textquotesingle in the context of deep learning. We show that averaging gradients across examples -- akin to a logical OR of patterns -- can favor memorization and \textasciigravepatchwork\textquotesingle solutions that sew together different strategies, instead of identifying invariances. To inspect this, we first formalize a notion of consistency for minima of the loss surface, which measures to what extent a minimum appears only when examples are pooled. We then propose and experimentally validate a simple alternative algorithm based on a logical AND, that focuses on invariances and prevents memorization in a set of real-world tasks. Finally, using a synthetic dataset with a clear distinction between invariant and spurious mechanisms, we dissect learning signals and compare this approach to well-established regularizers.}

{Why we learn, what we learn, what the conditions are under which we learn, how the learning happens, and what the answers to these questions imply about how we should teach are the topics of this urgent, compelling, and highly multidisciplinary book by Stanislas Dehaene. He is one of the world\textquoterights most storied and celebrated cognitive scientists and cognitive neuroscientists. Along the way, we get to enjoy some mental gymnastics concerning the joint roles of nature and nurture, a wealth of revealing and sometimes heart-wrenching individual examples, and a glimpse into the potential potency but current impoverishment of machine learning systems.}

{How do people learn complex rules? We introduce a novel paradigm called \textquotedblrightTrack-A-Mole\textquotedblright, in which participants have to learn about and predict the moves of a cartoon mole, whose movements are generated by graphical programs. Our results show that participants can learn to predict richly structured programs, and often require only few observations to do so, showing rapid learning and early insights about the underlying patterns. Moreover, we found that how learnable a program is can be predicted by features related to its complexity and compressibility. Finally, participants also show interesting patterns of generalizations, assuming more parsimonious rules first and then gradually adjusting their predictions to more complex regularities, as well as matching their predictions to the general direction of movements and producing sensi- ble errors. These results extend our understanding of complex rule learning and open up future opportunities to model sequential pattern predictions as graphical program induction.}

{As we transition from child to adult, we navigate the worlddifferently. In this world, many of the relationships betweenevents are unclear or uncertain because they are probabilisticin nature. We wanted to know how learning about probabilis-tic relationships changes with development and to interrogatethe underlying processes. We investigated these questions in aprobabilistic reinforcement learning task (The Butterfly Task)with 302 participants aged 8-30. We found performance in thistask increased with age through early-twenties, then stabilized.Using hierarchical Bayesian methods to fit computational rein-forcement learning models, we showed that this performanceincrease was driven by 1) an increase in learning rate (i.e. de-crease in integration time horizon); 2) a decrease in exploratorychoices. By contrast, forgetting rates did not change with age.We discuss our findings in the context of other studies and hy-potheses about adolescent brain development.}