Publications

{Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such scale-free patterns were identified experimentally in many different types of neural recordings, the biological principles behind their emergence remained unknown. Utilizing different network models and motivated by experimental observations, synaptic plasticity was proposed as a possible mechanism to self-organize brain dynamics towards a critical point. In this review, we discuss how various biologically plausible plasticity rules operating across multiple timescales are implemented in the models and how they alter the network\textquotesingles dynamical state through modification of number and strength of the connections between the neurons. Some of these rules help to stabilize criticality, some need additional mechanisms to prevent divergence from the critical state. We propose that rules that are capable of bringing the network to criticality can be classified by how long the near-critical dynamics persists after their disabling. Finally, we discuss the role of self-organization and criticality in computation. Overall, the concept of criticality helps to shed light on brain function and self-organization, yet the overall dynamics of living neural networks seem to harnesses not only criticality for computation, but also deviations thereof.}

{The observation of histopathology using optical microscope is an essential procedure for examination of tissue biopsies or surgically excised specimens in biological and clinical laboratories. However, slide-based microscopic pathology is not suitable for visualizing the large-scale tissue and native 3D organ structure due to its sampling limitation and shallow imaging depth. Here, we demonstrate serial optical coherence microscopy (SOCM) technique that offers label-free, high-throughput, and large-volume imaging of ex vivo mouse organs. A 3D histopathology of whole mouse brain and kidney including blood vessel structure is reconstructed by deep tissue optical imaging in serial sectioning techniques. Our results demonstrate that SOCM has unique advantages as it can visualize both native 3D structures and quantitative regional volume without introduction of any contrast agents.}

{Over the last decades, multiple studies have reported signatures of criticality observed in various neuronal recordings. Moreover, theoretical investigations demonstrate that multiple aspects of information processing are optimized at the second-order phase transition. These studies motivated the hypothesis that the brain operates close to a critical state. To evaluate how distance from criticality may influence neural computations, researchers typically considered neural models that can attain various states (including critical and non-critical) depending on control parameters (e.g. connection-strength) and quantified how general information processing capabilities such as sensitivity to input, dynamic range, or information-transmission depend on these parameters. Certainly, being in a state with such optimized capabilities are relevant for the computations in the brain, but they are too abstract to provide a concrete implementation. For instance, all the mentioned capabilities are relevant for coding sensory information, but mere adjusting for the closeness to criticality cannot provide a neural implementation for coding given resource constraints. Whereas, frameworks like efficient coding both provide the objective to maximize and the implementation. Therefore, we introduce a novel complementary approach. We study a network that implements efficient coding and we investigate the presence of the scale-free neuronal avalanches in an optimized network. We consider a network of LIF neurons with synaptic transmission delays whose connectivity and dynamics are optimized for efficient coding. Previously, it was shown that the performance of such networks varies non-monotonically with the noise amplitude. We consider networks with different noise amplitudes and evaluate how close they are to a critical state by measuring deviations from the nearest power-law of avalanche size distributions. Interestingly, only in the optimized network the distribution of avalanche sizes truly follow a power-law. This result has important implications, as it shows how two influential, and previously disparate fields - efficient coding, and criticality - might be intimately related.}

{Learning and generalization in spatial domains is often thought to rely on a \textquotedblleftcognitive map\textquotedblright, representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions.}

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 the inactivation of spike generation how these interact with subthreshold and spiking dynamics. Including these constraints, the 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 open the possibility of future large-scale simulations 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.

{CEST MRI provides metabolite-based contrasts but often suffers from poor volume coverage or spatial resolution. We optimized and included a snapshot 3D-EPI readout and propose a suitable post-processing pipeline to generate CEST contrast in the whole brain at clinical B0\textequals3T. It is shown that CEST MRI with 1.8mm isotropic nominal resolution at a field of view of 256x224x156mm\mbox{$^3$} is feasible within 4.3s per presaturation frequency offset. The approach is adaptable for any presaturation scheme. Exemplarily low power saturation was performed and fitted Lorentzian amplitudes gave a coefficient of variation \textless8.5\textpercent across three healthy subjects.}

{MRI has emerged as a very important tool in biomedical research and is an essential diagnostic method in clinical radiology today. Although it can be utilised as a standalone technique, the inherent low sensitivity of the method has led to the development of contrast agents (CAs) in order to improve the specificity of the measurement. Nevertheless, the preparation of such probes is often challenging using standard solution phase chemistry, resulting in limitations in CA diversity and ultimately their broader applications. Solid phase synthesis (SPS) has emerged as an alternative synthetic methodology that can assist in circumventing these issues to enable more complex and specific derivatives to be developed. This article aims to provide a concise overview of the strategies employed for MRI CAs developed using SPS synthetic methodologies and evaluate the outlook for the approach in future CA synthesis. Specifically, the development of ligands for T1-weighted imaging, chemical exchange saturation transfer and bioresponsive MRI CAs synthesised directly via SPS are discussed.}

{Introduction: The deep layers of superior colliculus (SC) integrate sensory information from multiple modalities to create a coherent sensory representation of the world [1,2]. However, information about the human SC is limited due to the technical challenge of imaging small structures deep within the cranium [3]. Advances in ultra-high field MRI enable imaging with greater signal-to-noise ratio in smaller voxels, allowing us to probe functional responses within SC [4]. To understand how human SC integrates information across somatosensory and visual modalities, we utilized functional MRI (fMRI) at 9.4T during an integration task. Methods: We collected fMRI from 5 individuals at 9.4T using a 16-channel transmit/31 receive array [5]. Participants performed a somatovisual integration task in which air puffs delivered to their fingers cued them to attend (but not saccade) to a quadrant of the visual field. Participants were asked to count the number of "+" signs that appeared in dot patterns in the cued quadrant while ignoring "X" and other random patterns. Single air puffs were continuously alternately presented to the index and ring fingers; a random double air puff cued visual attention to the upper (via index finger stimulation) or lower (via ring finger stimulation) visual fields. Stimulation alternated between the left and right hands (cuing left and right visual fields) every 15 seconds, enabling sinoidal data analysis. In one participant, a second session used a visually cued paradigm with no tactile stimulation, allowing us to compare visual-only to somatovisual collicular processing. Functional images (point-spread function-corrected EPI) were collected with 1 mm isotropic voxels over 26 slices which include the colliculi and most of early visual cortex (TR \textequals 1.25 s). T1-weighted anatomical images were acquired with an MP2RAGE sequence (0.6-mm isotropic voxels). Brain regions were initially segmented from the T1-weighted images using FreeSurfer, followed by manual adjustment. Next, a level-set depth-mapping approach was used to compute unique associations-streamlines-from the collicular surface to the cerebral aqueduct, enabling quantification of BOLD responses as a function of collicular depth. Functional data were processed using a variant of the MrVista package. We corrected data for slice timing and motion and then fit a sinusoid (with frequency matching the left-right stimulus alternation) to each voxel time series, extracting amplitude, phase, and coherence. Next, using the depth streamlines, we averaged responses at superficial (0.6\textendash1.8 mm) and deep (3.5\textendash5.5 mm) levels. Results: We found strong lateralization of BOLD responses in the SC in all participants, with the attended visual hemifield having increased contralateral collicular activity. Activation was widespread in rostral SC at multiple depths. In rostral SC at superficial depths, BOLD responses were strongly lateralized, contralateral to the attended visual stimulus. In caudal SC, where deep somatosensory processing is expected for fore-limb stimulation [1], we saw activation in at least one SC (Figure 1 bottom). Indeed, compared to the BOLD phase in a visual-only task, deep caudal SC was significantly stronger (p \textless 0.01) in the somatovisual integration task (Figure 2). Conclusions: Using high resolution fMRI, we identified regions in SC that respond to somatovisual integration. These correspond to deep layers of the SC, which are believed to represent multisensory information onto a visuotopic map. In confirmation, a visual-only version of the task resulted in much weaker responses in deep caudolateral SC, while maintaining strong responses in the rostral superficial SC, corresponding to predominantly visual layers that represent the visual stimulus. Overall, we found that ultra-high field fMRI is sensitive to somatosensory integration in deep layers of human superior colliculus, which to this point has only been accessible in animal models.}

{Decisions to avoid or escape predators occur at different spatiotemporal scales, resulting in different computations and neural circuits. At their extremes, surprising or proximal threats will reduce decision and state space and utilize model-free architectures, while distant threats allow increased information processing supported by model-based operations. Model-free and model-based computations, however, are often intertwined. Furthermore, under conditions of safety the foundations for effective reactive execution in the future can be laid through model-based instruction of model-free control. Prospective planning can also be enabled. Together, these computations reflect distinct population codes embedded within a distributed defensive circuitry whose goal is to determine and realize the best policy. Naturalistic observations show that decisions to avoid or escape predators occur at different spatiotemporal scales and that they are supported by different computations and neural circuits. At their extremes, proximal threats are addressed by a limited repertoire of reflexive and myopic actions, reflecting reduced decision and state spaces and model-free (MF) architectures. Conversely, distal threats allow increased information processing supported by model-based (MB) operations, including affective prospection, replay, and planning. However, MF and MB computations are often intertwined, and under conditions of safety the foundations for future effective reactive execution can be laid through MB instruction of MF control. Together, these computations are associated with distinct population codes embedded within a distributed defensive circuitry whose goal is to determine and realize the best policy.}

{With fast development of recording techniques, simultaneous recordings of large groups of neurons reveal widely distributed spatiotemporal neural correlations in the cortex. Pairwise neural correlations are related to functional properties of neurons. They affect sensory information processing, learning and plasticity, and cognitive functions such as attention. At the population level, the spatial and temporal modes of correlations intermingle, which possibly reflects underlying anatomical circuit structure, network dynamics and operating regimes of neural activity. However, a systematic approach to disentangle the mixed patterns of spatial and temporal modes in correlations has not been fully developed. Here we develop a theoretical framework that relates the spatial and temporal modes of pairwise neural correlations to the network connectivity structure and the operating regime of dynamics in interacting neurons. We analyze spatiotemporal correlations in network models of binary units with different connectivity structures and dimensions. We derive analytical expressions for spatial and temporal correlations and verify them with numerical simulations. Our theory demonstrates how multiple timescales in auto- and cross-correlations arise from spatial interactions between units. We find that because of spatial dependence of interactions, each timescale is associated with fluctuations of a particular spatial frequency and makes hierarchical contributions to the correlations. We then study how local versus distributed spatial connectivity shapes the timescales and spatial patterns of neural correlations. finally, we evaluate the influence of external inputs on the operating regime of the global network activity and show how it affects the timescales of correlations. Our work reveals the relationship between spatial and temporal patterns of correlations, which is determined by the network structure, dynamics and the operating regime of population activity. Analytical methods developed here can be used to extract and interpret spatiotemporal features of neural dynamics during sensory and cognitive processing, to advance understanding of neural circuit functions.}

{This study presents the first investigations with algorithmic spinal cord-segmentation, as well as gray matter/white matter-segmentation within the spinal cord, at the ultrahigh field strength of 9.4T. On multi-echo gradient-echo acquisitions from three subjects, the tested algorithms perform the segmentations correctly. Based on these multi-echo data, pixel-wise T2\textasteriskcentered-relaxation time maps were calculated. By means of the segmentations, averaged T2\textasteriskcentered-times of 24.88ms +- 6.68ms for gray matter and 19.37ms +- 8.66ms for white matter, were calculated.}

{During infancy, the human brain rapidly expands in size and complexity as neural networks mature and new information is incorporated at an accelerating pace. Recently, it was shown that single-electrode EEG in preterms at birth exhibits scale-invariant intermittent bursts. Yet, it is currently not known whether the normal infant brain, in particular, the cortex, maintains a distinct dynamical state during development that is characterized by scale-invariant spatial as well as temporal aspects. Here we employ dense-array EEG recordings acquired from the same infants at 6 and 12 months of age to characterize brain activity during an auditory odd-ball task. We show that suprathreshold events organize as spatiotemporal clusters whose size and duration are power-law distributed, the hallmark of neuronal avalanches. Time series of local suprathreshold EEG events display significant long-range temporal correlations (LRTCs). No differences were found between 6 and 12 months, demonstrating stability of avalanche dynamics and LRTCs during the first year after birth. These findings demonstrate that the infant brain is characterized by distinct spatiotemporal dynamical aspects that are in line with expectations of a critical cortical state. We suggest that critical state dynamics, which theory and experiments have shown to be beneficial for numerous aspects of information processing, are maintained by the infant brain to process an increasingly complex environment during development.}

{Monte-Carlo Tree Search (MCTS) is one of the most-widely used methodsfor planning, and has powered many recent advances in artificialintelligence. In MCTS, one typically performs computations(i.e., simulations) to collect statistics about the possible futureconsequences of actions, and then chooses accordingly. Manypopular MCTS methods such as UCT and its variants decide whichcomputations to perform by trading-off exploration and exploitation. Inthis work, we take a more direct approach, and explicitly quantify thevalue of a computation based on its expected impact on the quality ofthe action eventually chosen. Our approach goes beyond the \emph\textbraceleftmyopic\textbracerightlimitations of existing computation-value-based methods in two senses:(I) we are able to account for the impact of non-immediate (ie, future)computations (II) on non-immediate actions. We show that policies thatgreedily optimize computation values are optimal under certainassumptions and obtain results that are competitive with the state-of-the-art.}

{Balanced steady-state free precession imaging has recently been suggested for chemical exchange detection (bSSFPX). The objective of this work is to investigate the contributions of microstructural, chemical shift and chemical exchange effects to the asymmetry of the bSSFP profile at field strengths of 3 T and 9.4 T. To this end, in vitro bSSFPX experiments are performed for a range of repetition times and flip angles in glucose water solutions with different MnCl2 concentrations and tissue homogenates obtained from the brainstem of pig brains. The experimental results are compared to multi-pool Bloch-McConnell simulations. Additionally, the influence of white matter tract geometry is analyzed ex vivo in pig brain hemispheres measured at two different angles with respect to B0. The detectable bSSFP profile asymmetry in glucose solutions with tissue-like relaxation times and white matter homogenates was consistent with Bloch-McConnell simulations but relatively small. In intact white matter tracts, the asymmetry was dominated by structural effects with a strong dependency on tract orientation relative to B0. In tracts perpendicular to B0, the asymmetry was $\approx$ 3-4 times higher than in the homogenates, thus barely affected by chemical exchange effects. In conclusion, chemical exchange-related bSSFP profile asymmetries are detectable in tissue homogenates, however, the observed asymmetry level is generally low and prone to confounding structural effects rendering in vivo chemical exchange detection with bSSFP challenging in the brain.}

{Digitized neuroanatomical atlases are crucial for localizing brain structures and analyzing functional networks identified by magnetic resonance imaging (MRI). To aid in MRI data analysis, we have created a comprehensive parcellation of the rhesus macaque subcortex using a high-resolution ex vivo structural imaging scan. The structural scan and its parcellation were warped to the updated NIMH Macaque Template (NMT v2), an in vivo population template, where the parcellation was refined to produce the Subcortical Atlas of the Rhesus Macaque (SARM). The subcortical parcellation and nomenclature reflect those of the 4th edition of the Rhesus Monkey Brain in Stereotaxic Coordinates (RMBSC4; Paxinos et al., in preparation). The SARM features six parcellation levels, arranged hierarchically from fine regions-of-interest (ROIs) to broader composite regions, suited for fMRI studies. As a test, we ran a functional localizer for the dorsal lateral geniculate (DLG) nucleus in three macaques and found significant fMRI activation in this atlas region. The SARM has been made openly available to the neuroimaging community and can easily be used with common MR data processing software, such as AFNI, where the atlas can be embedded into the software alongside cortical macaque atlases.}

{Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with adolescent-onset MDD ($\leq$ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen\textquotesingles d \textequals -0.164 to -0.180). Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen\textquotesingles d \textequals -0.173 to -0.184). Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.}

{PURPOSE: Relaxation times can contribute to spectral assignment. In this study, effective T2 relaxation times ( Teff2 ) of macromolecules are reported for gray and white matter-rich voxels in the human brain at 9.4 T. The Teff2 of macromolecules are helpful to understand their behavior and the effect they have on metabolite quantification. Additionally, for absolute quantification of metabolites with magnetic resonance spectroscopy, appropriate T2 values of metabolites must be considered. The T2 relaxation times of metabolites are calculated after accounting for TE/sequence-specific macromolecular baselines. METHODS: Macromolecular and metabolite spectra for a series of TEs were acquired at 9.4 T using double inversion-recovery metabolite-cycled semi-LASER and metabolite-cycled semi-LASER, respectively. The T2 relaxation times were calculated by fitting the LCModel relative amplitudes of macromolecular peaks and metabolites to a mono-exponential decay across the TE series. Furthermore, absolute concentrations of metabolites were calculated using the estimated relaxation times and internal water as reference. RESULTS: The Teff2 of macromolecules are reported, which range from 13 ms to 40 ms, whereas, for metabolites, they range from 40 ms to 110 ms. Both macromolecular and metabolite T2 relaxation times are observed to follow the decreasing trend, with increasing B0 . The linewidths of metabolite singlets can be fully attributed to T2 and B0 components. However, in addition to these components, macromolecule linewidths have contributions from J-coupling and overlapping resonances. CONCLUSION: The T2 relaxation times of all macromolecular and metabolite peaks at 9.4 T in vivo are reported for the first time. Metabolite relaxation times were used to calculate the absolute metabolite concentrations.}

{Purpose: T2-weighted signal hyperintensities in white matter (WM) are a diagnostic finding in brain magnetic resonance imaging (MRI) of patients with metachromatic leukodystrophy (MLD). In our systematic investigation of the evolution of T2-hyperintensities in patients with the late-infantile form, we describe and characterize T2-pseudonormalization in the advanced stage of the natural disease course. Methods: The volume of T2-hyperintensities was quantified in 34 MRIs of 27 children with late-infantile MLD (median age 2.25 years, range 0.5-5.2 years). In three children with the most advanced clinical course (age \textgreater4 years) and for whom the T2-pseudonormalization was the most pronounced, WM microstructure was investigated using a multimodal MRI protocol, including diffusion-weighted imaging, MR spectroscopy (MRS), myelin water fraction (MWF), magnetization transfer ratio (MTR), T1-mapping and quantitative susceptibility mapping. Results: T2-hyperintensities in cerebral WM returned to normal in large areas of 3 patients in the advanced disease stage. Multimodal assessment of WM microstructure in areas with T2-pseudonormalization revealed highly decreased values for NAA, neurite density, isotropic water, mean and radial kurtosis, MWF and MTR, as well as increased radial diffusivity. Conclusion: In late-infantile MLD patients, we found T2-pseudonormalization in WM tissue with highly abnormal microstructure characterizing the most advanced disease stage. Pathological hallmarks might be a loss of myelin, but also neuronal loss as well as increased tissue density due to gliosis and accumulated storage material. These results suggest that a multimodal MRI protocol using more specific microstructural parameters than T2-weighted sequences should be used when evaluating the effect of treatment trials in MLD.}

{TReND is a volunteer-scientist run charity dedicated to promoting research and education on the African continent. Focusing on neuroscience, we discuss approaches to address some of the factors that currently stifle Africa\textquotesingles scientific development and our experience in implementing them.}

The dynamic range of stimulus processing in living organisms is much larger than a single neural network can explain. For a generic, tunable spiking network we derive that while the dynamic range is maximal at criticality, the interval of discriminable intensities is very similar for any network tuning due to coalescence. Compensating coalescence enables adaptation of discriminable intervals. Thus, we can tailor an ensemble of networks optimized to the distribution of stimulus intensities, e.g., extending the dynamic range arbitrarily. We discuss potential applications in machine learning.

{Depression is one of the largest contributors to the burden of disease worldwide and better understanding of the disorder is needed. One promising direction is the use of cognitive phenomena, such as temporal discounting, as intermediate phenotypes revealing its underlying nature and as markers of therapeutic success. We administered a temporal discounting task and the Beck\textquoterights Depression Inventory (BDI) to 170 in-ward patients from the Psychiatric University Hospital in Erlangen and to 176 healthy controls. All patients fulfilled the ICD-10 criteria for moderate to severe depression. Healthy controls had sub-threshold scores for depression and had no neuropsychiatric history. We first examined group differences in the mean responses and in the inconsistency of the choices. Then we used Bayesian information criterion (BIC) to compare 3 models of delay discounting all based on the hyperbolical discounting model Q(R,D,k)\textequalsR1+k$\ast$D where R \textequalsreward, k\textequalsdiscount rate, and D\textequalsdelay, comparing the subjective value of the immediate option Qi\textequalsQ(Ri,0,k) against the delayed option Qd\textequalsQ(Rd,D,k), and generating the probability of choosing the immediate option pi\textequals1\textminuspd, where softmax $\sigma$($\zeta$)\textequals11+exp(\textminus$\zeta$). (1) preference-temperature model (k, $\beta$): pi\textequals$\sigma$($\beta$(Qi\textminusQd)) (2) preference-uncertainty model ($\micro$,$\sigma$): draw:log(ks)fromN($\micro$,$\sigma$): if:Qi\textgreaterQ(Rd,D,ks),pi\textequals1 otherwise:pi\textequals0 (3) trembling-hand model (k, $\beta$, lapse): $\lambda$\textequals(356)$\sigma$(lapse)+1e\textminus4 pi\textequals(1\textminus2$\lambda$)$\sigma$($\beta$(Qi\textminusQd))+$\lambda$ Model-agnostic analysis showed no group difference in the mean responses (t-test: t\textequals1.686, p\textequals0.092), but choosing the immediate reward was correlated with BDI (Pearson: r\textequals0.248, p\textequals5.382e-06). Patients were less consistent in their responses (t\textequals-1.9963, p\textequals0.046), but the inconsistency was not correlated with BDI (r\textequals0.092, p\textequals0.093). Model (1) yielded the best fit (BIC(1)\textequals3.817, BIC(2)\textequals3.893, BIC(3)\textequals7.001). There was no significant group difference in the discount rate k (t\textequals1.074, p\textequals0.284) or inverse temperature $\beta$ (t\textequals-0.263, p\textequals0.793) and there was no correlation of k or $\beta$ with BDI (k: r\textequals 0.0866, p\textequals0.117; $\beta$: r\textequals-0.071, p\textequals0.197). We conclude that depressed patients were more inconsistent in their choices and depressiveness was correlated with choosing immediate over delayed rewards. One limitation of our study was that the delay discounting task was hypothetical and covered only a limited range of discount rates.}

{Background The optimization of magnetic resonance spectroscopy (MRS) sequences allows improved diagnosis and prognosis of neurological and psychological disorders. Thus, to assess the test\textendashretest and intersequence reliability of such MRS sequences in quantifying metabolite concentrations is of clinical relevance. Purpose To evaluate the test\textendashretest and intersequence reliability of three MRS sequences to estimate GABA and Glx \textequals Glutamine+Glutamate concentrations in the human brain. Study Type Prospective. Subjects Eighteen healthy participants were scanned twice (range: 1 day to 1 week between the two sessions) with identical protocols. Field Strength/Sequence 3T using a 32-channel SENSE head coil in the PCC region; PRESS, JPRESS, and MEGA-PRESS sequences. Assessment Metabolite concentrations were estimated using LCModel (for PRESS and MEGA-PRESS) and ProFit2 (for JPRESS). Statistical Tests The test\textendashretest reliability was evaluated by Wilcoxon signed-rank tests, Pearson\textquotesingles r correlation coefficients, intraclass-correlation coefficients (ICC), coefficients of variation (CV), and by Bland\textendashAltman (BA) plots. The intersequence reliability was assessed with Wilcoxon signed-rank tests, Pearson\textquotesingles r correlation coefficients, and BA plots. Results For GABA, only the MEGA-PRESS sequence showed a moderate test\textendashretest correlation (r \textequals 0.54, ICC \textequals 0.5, CV \textequals 8.8\textpercent) and the BA plots indicated good agreement (P \textgreater 0.05) for all sequences. JPRESS provided less precise results and PRESS was insensitive to GABA. For Glx, the r and ICC values for PRESS (r \textequals 0.87, ICC \textequals 0.9, CV \textequals 2.9\textpercent) and MEGA-PRESS (r \textequals 0.70, ICC \textequals 0.7, CV \textequals 5.3\textpercent) reflect higher correlations, compared with JPRESS (r \textequals 0.39, ICC \textequals 0.4, CV \textequals 20.1\textpercent).}

{The limbic system is a phylogenetically old, behaviorally defined system that serves as a center for emotions. It controls the expression of anger, fear, and joy and also influences sexual behavior, vegetative functions, and memory. The system comprises a collection of tel-, di-, and mesencephalic structures whose components have evolved and increased over time. Previous animal research indicates that the anterior nuclear group of the thalamus (ANT), as well as the habenula (Hb) and the adjacent mediodorsal nucleus (MD) each play a vital role in the limbic circuitry. Accordingly, diffusion imaging data of 730 subjects obtained from the Human Connectome Project and the masks of six nuclei (anterodorsal, anteromedial, anteroventral, lateral dorsal, Hb, and MD) served as seed regions for a direct probabilistic tracking to the rest of the brain using diffusion-weighted imaging. The results revealed that the ANT nuclei are part of the limbic and the memory system as they mainly connect via the mammillary tract, mammillary body, anterior commissure, fornix, and retrosplenial cortices to the hippocampus, amygdala, medio-temporal, orbito-frontal and occipital cortices. Furthermore, the ANT nuclei showed connections to the mesencephalon and brainstem to varying extents, a pattern rarely described in experimental findings. The habenula-usually defined as part of the epithalamus-was closely connected to the tectum opticum and seems to serve as a neuroanatomical hub between the visual and the limbic system, brainstem, and cerebellum. Finally, in contrast to experimental findings with tracer studies, directly determined connections of MD were mainly confined to the brainstem, while indirect MD fibers form a broad pathway connecting the hippocampus and medio-temporal areas with the mediofrontal cortex.}

{Social norms represent a fundamental grammar of social interactions, as they refer to shared expectations about behaviors of one\textquotesingles social group members (Bicchieri, 1990, 2005; Santos et al., 2018). Based on these expectations, particularly accurate predictions about another person\textquotesingles future behavior are possible\textemdashestablishing the preconditions for cooperative interactions. Overall, group prosperity is enhanced when all members comply with social norms (i.e., norm compliance). However, social norms need to be enforced by sanctioning violators (i.e., norm enforcement). For instance, expectations of compliance with a norm of reciprocity may help overcome the fear of being betrayed by a social partner. As cooperation allows for better collective solutions than those attained by self-interested individuals, social groups are interested in enforcing compliance with social norms by their members, and developing tools for successful recognition of norm violators (Fehr and Schurtenberger, 2018). Thus, a fragile balance between incentives for norm enforcement and deterrents for sanctions of violators is required for a well-functioning society.}

{Social punishment (SOP)-third-party punishment (TPP) and second-party punishment (SPP)-sanctions norm-deviant behavior. The hierarchical punishment model (HPM) posits that TPP is an extension of SPP and both recruit common processes engaging large-scale domain-general brain networks. Here, we provided meta-analytic evidence to the HPM by combining the activation likelihood estimation approach with connectivity analyses and hierarchical clustering analyses. Although both forms of SOP engaged the dorsolateral prefrontal cortex and bilateral anterior insula (AI), a functional differentiation also emerged with TPP preferentially engaging social cognitive regions (temporoparietal junction) and SPP affective regions (AI). Further, although both TPP and SPP recruit domain-general networks (salience, default-mode, and central-executive networks), some specificity in network organization was observed. By revealing differences and commonalities of the neural networks consistently activated by different types of SOP, our findings contribute to a better understanding of the neuropsychological mechanisms of social punishment behavior--one of the most peculiar human behaviors.}

{Consider a gray field comprising pairs of vertically aligned dots; in each pair, one dot is white the other black. When viewed in a peripheral visual field, these pairs appear horizontally aligned. By the Central-Peripheral Dichotomy, this flip tilt illusion arises because top-down feedback from higher to lower visual cortical areas is too weak or absent in the periphery to veto confounded feedforward signals from the primary visual cortex (V1). The white and black dots in each pair activate, respectively, on and off subfields of V1 neural receptive fields. However, the sub-fields\textquotesingle orientations, and the preferred orientations, of the most activated neurons are orthogonal to the dot alignment. Hence, V1 reports the flip tilt to higher visual areas. Top-down feedback vetoes such misleading reports, but only in the central visual field.}

{A variety of conceptualizations of psychological uncertaintyexist. From an information-theoretic perspective, probabilisticuncertainty can be formalized as mathematical entropy. Cog-nitive emotion theories posit that uncertainty appraisals andmotivation to reduce uncertainty are modulated by emotionalstate. Yet little is known about how people evaluate proba-bilistic uncertainty, and about how emotional state modulatespeople\textquoterights evaluations of probabilistic uncertainty and behaviorto reduce probabilistic uncertainty. We tested intuitive entropyevaluations and entropy reduction strategies across four emo-tion conditions in the Entropy Mastermind game. We used theunified Sharma-Mittal space of entropy measures to quantifyparticipants\textquoteright entropy evaluations. Results suggest that manypeople use a heuristic strategy, focusing on the number of pos-sible outcomes, irrespective of the probabilities in the proba-bility distribution. This result is surprising, given that previouswork suggested that people are very sensitive to the maximumprobability when choosing queries on probabilistic classifica-tion tasks. Emotion induction generally increased participants\textquoterightheuristic assessment. The uncertainty associated with emo-tional states also affected game play: participants needed fewerqueries and spent less time on games in high-uncertainty thanin low-uncertainty emotional states. Yet entropy perceptionswere not related to subjectively reported uncertainty, numer-acy or entropy knowledge, suggesting that entropy perceptionsmay form an independent psychological construct.}

{Information about the probability of an event is a fundamental component of our everyday life and affects how certain we feel when making a decision or predicting the future. Often, probabilistic uncertainty is communicated as risk, for example when a tracking app tells us about our risk of being infected with COVID-19. Previous research has demonstrated that risk perceptions are affected by emotional states: emotion-specific appraisal patterns, among them uncertainty appraisals, influence evaluations of risk and modulate risk reducing behaviour. At the core of risks are probabilities and evaluating probability distributions is fundamental to assessing risks. Yet, how emotions affect people\textquoterights basic evaluations of probabilities is an open question. I present research on a) the relationship between anxiety, subjective evaluations of uncertainty and quantitative estimates of neutral probabilistic events during the Coronavirus-pandemic and b) people\textquoterights evaluations of probabilistic uncertainty (entropy) in different experimentally induced emotional states (anxiety, pride and anger).}

{Human gait patterns are rich in socially relevant information. While many studies have investigated sex-specific differences in walking style, little is known about how sexual dimorphism relates to the perceived attractiveness and confidence of a person. In two studies, 40 observers (20 female, 20 male) rated the attractiveness and another 36 observers (18 female, 18 male) rated the confidence of 50 men and 50 women from the bmlRUB motion capture database, each presented in three different ways in virtual reality: (a) as a 3D virtual character with each actor\textquotesingles individual shape and walking motion reconstructed from optical motion capture data using the MoSh algorithm (Loper et al. 2014, SIGGRAPH Asia), (b) as a static virtual character, and (c) as a walking stick-figure (Troje 2002, JOV). Correlations between all 12 sets of ratings (2 walker sex x 2 participant sex x 3 presentation types) of the two datasets revealed that sexual dimorphism in walking style plays a different role in male and female walkers for attractiveness and confidence ratings. Sexual dimorphism dominates female attractiveness and male confidence assigned to animated virtual characters and stick-figures. The more feminine a woman walks, the more attractive she is rated; the more masculine a man walks, the more confident he is rated. Perceived male attractiveness and female confidence, on the other hand, are determined by increased vertical body movements which make the walkers appear bouncy and energetic. High ratings of the static virtual characters are characterised by tall and slim body shapes for male and female attractiveness, and female confidence, and tall and strong body shapes for male confidence (as compared to small and heavy body shapes). Sexual dimorphism seems to play a different role in attributing biological and personality traits to male and female walkers, but male and female observers agree on their ratings.}

{Host-associated microbiotas normally guide the trajectory of intrinsically encoded developmental programs, and dysbiosis is linked to neurodevelopmental disorders such as autism spectrum disorder. Recent work suggests that microbiotas modulate social phenotypes associated with these disorders, though developmental mechanisms linking microbiotas to social behavior are not well understood. We discovered that the zebrafish microbiota is required for normal social behavior. Using this model to examine neuronal features modulated by the microbiota during early development, we found that the microbiota restrains neurite complexity and targeting of specific forebrain neurons required for normal social behavior. The microbiota is also required for normal forebrain infiltration of microglia, the brain\textquoterights resident phagocytes that remodel neuronal arbors, suggesting the microbiota modulates arborization via a neuro-immune route. Our work establishes a foundation for study of microbial and host mechanisms that link the microbiota and social behavior in an experimentally tractable model vertebrate.}

{Previous studies have identified relatively separated regions of the brain that respond strongly when participants view images of either faces, bodies or objects. The aim of this thesis was to investigate how and where in the brain shared properties of faces, bodies and objects are processed. We selected three properties that are shared by faces and bodies, shared categories (sex and weight), shared identity and shared orientation (i.e. facing direction). We also investigated one property shared by faces and objects, the tendency to process a face or object as a whole rather than by its parts, which is known as holistic processing. We hypothesized that these shared properties might be encoded separately for faces, bodies and objects in the previously defined domain-specific regions, or alternatively that they might be encoded in an overlapping or shared code in those or other regions. In all of studies in this thesis, we used fMRI to record the brain activity of participants viewing images of faces and bodies or objects that showed differences in the shared properties of interest. We then investigated the neural responses these stimuli elicited in a variety of specifically localized brain regions responsive to faces, bodies or objects, as well as across the whole-brain. Our results showed evidence for a mix of overlapping coding, shared coding and domain-specific coding, depending on the particular property and the level of abstraction of its neural coding. We found we could decode face and body categories, identities and orientations from both face- and body-responsive regions showing that these properties are encoded in overlapping brain regions. We also found that non-domain specific brain regions are involved in holistic face processing. We identified shared coding of orientation and weight in the occipital cortex and shared coding of identity in the early visual cortex, right inferior occipital cortex, right parahippocampal cortex and right superior parietal cortex, demonstrating that a variety of brain regions combine face and body information into a common code. In contrast to these findings, we found evidence that high-level visual transformations may be predominantly processed in domain-specific regions, as we could most consistently decode body categories across image-size and body identity across viewpoint from body-responsive regions. In conclusion, this thesis furthers our understanding of the neural coding of face, body and object properties and gives new insights into the functional organisation of occipitotemporal cortex.}

{Theoretical and experimental investigations of different neuronal systems suggest that operating close to a critical state can be beneficial for information processing. Particularly, the dynamic range (range of inputs an ideal observer can decode from the output of the system) was shown to be maximized at the phase transition [1]. In the probabilistic recurrent network traditionally used to model neuronal systems tunable towards and away from the critical state, all interactions are excitatory. In this case, there is only one phase transition: between the no activity case and ceaseless activity. Interestingly, when the inhibitory interactions are added to the network [2], there are two transition points [3]: from no activity to the sustained finite activity, and from finite activity to the full system activation. We discover that both transitions are associated with locally increased dynamic range. Although the second transition results in the overall largest dynamic range. There is, however, a caveat in the current dynamic range definition for recurrent networks: it utilizes the mean response curve, and thus requires an infinite observation time. However, everyday decisions typically involve very short time intervals. Here we constrain the time the ideal observer is monitoring the output of the network. In this case, the noise corrupts the response, resulting in a distribution of mean outputs P(o\textbars$\ast$). This makes it impossible to reconstruct the presented input s$\ast$ with 100\textpercent certainty. Instead, we suggest that the input signals s1 and s2 can be discriminated if the minimal discriminator error between them is smaller than $\varepsilon$. For a given observation time, we can then determine the stimuli that can be reliably discriminated by the network. We call the interval of these stimuli the finite observation dynamic range. As the observation time goes to infinity, the classical dynamic range definition is recovered. We demonstrate that the finite observation dynamic range is not maximized for the critical excitatory network. Moreover, depending on the length of observation time, differently tuned systems become optimal: the shorter the time the more subcritical the optimal system becomes. Our results predict a diversity of subcritical tunings (with different timescales) in cortical networks, depending on the required reaction time. This diversity of timescales is in line with the reported hierarchy of timescales across the brain.}

{Inertial motions may be defined in terms of acceleration and jerk, the time-derivative of acceleration. We investigated the relative contributions of these characteristics to the perceived intensity of motions. Participants were seated on a high-fidelity motion platform, and presented with 25 above-threshold 1 s forward (surge) motions that had acceleration values ranging between 0.5 and 2.5 [Formula: see text] and jerks between 20 and 60 [Formula: see text], in five steps each. Participants performed two tasks: a magnitude estimation task, where they provided subjective ratings of motion intensity for each motion, and a two-interval forced choice task, where they provided judgments on which motion of a pair was more intense, for all possible combinations of the above motion profiles. Analysis of the data shows that responses on both tasks may be explained by a single model, and that this model should include acceleration only. The finding that perceived motion intensity depends on acceleration only appears inconsistent with previous findings. We show that this discrepancy can be explained by considering the frequency content of the motions, and demonstrate that a linear time-invariant systems model of the otoliths and subsequent processing can account for the present data as well as for previous findings.}

{Animals and humans replay neural patterns encoding trajectories through their environment, both whilst they solve decision-making tasks and during rest. Both on-task and off-task replay are believed to contribute to flexible decision making, though how their relative contributions differ remains unclear. We investigated this question by using magnetoencephalography (MEG) to study human subjects while they performed a decision-making task that was designed to reveal the decision algorithms employed. We characterised subjects in terms of how flexibly each adjusted their choices to changes in temporal, spatial and reward structure. The more flexible a subject, the more they replayed trajectories during task performance, and this replay was coupled with re-planning of the encoded trajectories. The less flexible a subject, the more they replayed previously preferred trajectories during rest periods between task epochs. The data suggest that online and offline replay both participate in planning but support distinct decision strategies.}

{Yttrium is a chemically versatile rare earth element that finds use in a range of applications including lasers and superconductors. In medicine, yttrium-based materials are used in medical lasers and biomedical implants. This is extended through the array of available yttrium isotopes to enable roles for 90Y complexes as radiopharmaceuticals and 86Y tracers for positron emission tomography (PET) imaging. The naturally abundant isotope 89Y is proving to be suitable for nuclear magnetic resonance investigations, where initial reports in the emerging field of hyperpolarised magnetic resonance imaging (MRI) are promising. In this review we explore the coordination and radiochemical properties of yttrium, and its role in drugs for radiotherapy, PET imaging agents and perspectives for applications in hyperpolarised MRI.}

{Having something to look forward to is a keystone of well-being. Anticipation of a future reward, like an upcoming vacation, can often be more gratifying than the very experience itself. Theories of anticipation have described how it induces behaviors ranging from beneficial information-seeking through to harmful addiction. However, it remains unclear how neural systems compute an attractive value from anticipation, instead of from the reward itself. To address this gap, we administered a decision-making task to human participants that allowed us to analyze brain activity during receipt of information predictive of future pleasant outcomes. Using a computational model of anticipatory value that captures participants\textquoteright decisions, we show that an anticipatory value signal is orchestrated by influences from three brain regions. Ventromedial prefrontal cortex (vmPFC) tracks the value of anticipation; dopaminergic midbrain responds to information that enhances anticipation, while sustained hippocampal activity provides a functional coupling between these regions. This coordinating function of the hippocampus is consistent with its known role in episodic future thinking. Our findings shed new light on the neural underpinnings of anticipation\textquoterights influence over decision-making, while also unifying a range of phenomena associated with risk and time-delay preference.}

{Humans continuously categorise inputs but only rarely receive explicit feedback as to whether or not they are correct. This implies that they may be engaged in semi-supervised learning, which integrates supervised and unsupervised adaption. However, experiments testing semi-supervised learning in humans are sparse, and bedevilled with conflicting results about the benefits of unsupervised information. Here, we suggest that one important factor that has been paid insufficient attention is the alignment between subjects\textquotesingle internal representations of the stimulus material, which is shaped by prior biases and experience, and the experimenter-defined representations that determine success in the tasks. Only if these representations match will unsupervised learning be successful. To test this hypothesis, we designed a series of behavioural categorisation experiments in which subjects initially categorise items along a salient, but task-irrelevant, dimension, and only recover the correct categories when sufficient feedback draws their attention to the subtle, task-relevant, stimulus dimensions. Withdrawing feedback at different stages of this learning curve tests whether unsupervised learning can improve performance when psychological stimulus space and task are sufficiently aligned, and whether the opposite is true if they are misaligned. Our predictions fit with work in perceptual and language learning where task proficiency has been reported to be a crucial predictor of the effects of further unsupervised learning. Our work implies that predicting and understanding human category learning in particular tasks requires assessment and consideration of the representational spaces that subjects entertain for the materials involved in those tasks. These considerations not only apply to studies in the lab, but could also help improve the design of tutoring systems and instruction.}

Ferrimagnetic iron garnets are promising materials for spintronics applications, characterized by ultralow damping and zero current shunting. It has recently been found that few nm-thick garnet films interfaced with a heavy metal can also exhibit sizable interfacial spin-orbit interactions, leading to the emergence, and efficient electrical control, of one-dimensional chiral domain walls. Two-dimensional bubbles, by contrast, have so far only been confirmed in micrometer-thick films. Here, we show by high resolution scanning transmission x-ray microscopy and photoemission electron microscopy that submicrometer bubbles can be nucleated and stabilized in ∼25-nm-thick thulium iron garnet films via short heat pulses generated by electric current in an adjacent Pt strip, or by ultrafast laser illumination. We also find that quasistatic processes do not lead to the formation of a bubble state, suggesting that the thermodynamic path to reaching that state requires transient dynamics. X-ray imaging reveals that the bubbles have Bloch-type walls with random chirality and topology, indicating negligible chiral interactions at the garnet film thickness studied here. The robustness of thermal nucleation and the feasibility demonstrated here to image garnet-based devices by x-rays both in transmission geometry and with sensitivity to the domain wall chirality are critical steps to enabling the study of small spin textures and dynamics in perpendicularly magnetized thin-film garnets.

{Cortical dynamics unfold across multiple timescales reflecting networks\textquoteright specialization for task-relevant computations [1,2]. However, it is unknown how these timescales emerge from the network connectivity and whether they can be flexibly modulated by cognitive demands, e.g., during attention. We analyzed the timescales in autocorrelations of population spiking activity recorded from single cortical columns in area V4 from monkeys performing a spatial attention task (AT) and a fixation task (FT) (fig 1A). We observed that both spontaneous (FT) and stimulus-driven (AT) activity exhibit two distinct timescales (one slow and one fast). To validate the presence of two timescales and estimate their values, we developed a method based on Approximate Bayesian Computations (ABC) [3]. Our method estimates the timescales from spiking activity by fitting autocorrelations using a generative model with multiple timescales to overcome statistical biases due to finite sample size [4] (fig 1B,C). We found that most recordings (31 out of 37, 84\textpercent) were better fitted with two timescales than one timescale. Moreover, the slow timescale was significantly longer on trials when monkeys attended to the receptive fields (RFs) location of the recorded neurons than on control trials when monkeys attended to a different location (fig 1C,D). We hypothesized that the observed timescales emerge from the recurrent network dynamics shaped by the spatial connectivity structure. We developed a network model consisting of binary units representing cortical minicolumns [5] with local spatial connectivity among them (fig 1E). We found that the activity of model minicolumns exhibits two distinct timescales: A fast timescale induced by vertical recurrent excitation within a minicolumn, and a slow timescale induced by horizontal interactions among minicolumns. Both timescales depend on the network topology, and the slow timescale disappears in networks with random connectivity (fig 1F). We derived an analytical relationship between the timescales and connectivity parameters, enabling us to identify model parameters best matching the timescales in the data. The model indicates that modulation of timescales during attention arises from a slight increase in the efficacy of horizontal interactions (fig 1G). Our results suggest that timescales of local neural dynamics emerge from the spatial network structure and can flexibly change due to top-down influences according to task demands.}

{Ongoing cortical dynamics unfold across different temporal scales. These timescales reflect the network\textquoterights specialization for task-relevant computations. However, it is unknown how different timescales emerge from the spatial network structure and whether they can be flexibly modulated by cognitive demands, e.g., during attention. We developed a network model which consists of binary units representing local neural populations (mini-columns) with spatially structured connections among them. We find that activity of the mini-columns exhibits two distinct timescales arising from the network dynamics. The first timescale is induced by recurrent excitation within a mini-column (vertical connectivity), and the second timescale is induced by interactions among mini-columns (horizontal connectivity). The timescales depend on the network topology, and the second timescale disappears in networks with random connectivity. To test model predictions, we analyzed spiking activity recorded from single cortical columns in the primate areas V1 and V4 during an attention task. We developed a novel method based on adaptive Approximate Bayesian Computations, which estimates the timescales from spiking activity and overcomes statistical biases due to finite sample size. We observed two timescales in both V1 and V4 population dynamics. Both timescales were significantly longer in V4 than in V1, which is explained by our model based on differences between V1 and V4 network properties. Moreover, the V1 and V4 timescales were longer when attention was directed toward neurons\textquoteright receptive-fields. This result reveals how ongoing network dynamics is influenced during top-down attention even without measurable modulations of firing rates in the absence of visual stimuli. Based on our model, modulation of timescales arises from an increase in efficacy of vertical connections and a slight suppression of horizontal interactions. Our results suggest that timescales of local neural dynamics emerge from the spatial network structure and can flexibly change due to top-down influences according to task demands.}

{Quantitative CEST imaging is still not applied in clinical routine, as both quantification and whole brain coverage require usually long scan times. In this work, we present a CEST-MRF protocol using spin-lock saturation pulses and a fast 3D-EPI readout with whole brain coverage. This enables a fast generation of quantitative amide proton concentration maps of the entire brain at a clinical scanner.}

{To fully understand the neurovascular fingerprint observed in BOLD experiments, extravascular and intravascular contributions have to be identified separately. Balanced steady-state free precession (bSSFP) imaging has demonstrated the ability for distortion-free fMRI with high microvascular sensitivity. However, the underlying intravascular contribution to BOLD bSSFP is not yet entirely known as literature R2 relaxation rates do not reflect the apparent diffusion-related R2 decrease in blood with shorter bSSFP refocusing intervals (TRs). This work thus focuses on characterizing the oxygen sensitivity of bSSFP in blood samples at high to ultra-high fields by means of passband signal differences and intrinsic R2 estimation.}

{Strong background signals leading to multiple phase wraps may hamper accurate quantification of magnetic tissue susceptibility (QSM) especially at high field strengths using long echo times to achieve a high spatial sampling. Here we show how different coil-combination, automated tissue masking and background removal techniques can be used to improve QSM quality. Performance was evaluated with regard to iron quantification in subcortical and cortical areas in the same subjects. We found a substantial improvement in accuracy and precision of QSM in high-field applications at long echo times through the use of ASPIRE and removal of areas with excessive phase evolution.}

Miniature magnetic sensors based on magnetoplasmonic crystals (MPlCs) exhibit high sensitivity and high spatial resolution, which can be obtained by the excitation of surface plasmon polaritons. A field dependence of surface plasmon polaritons' enhanced magneto-optical response strongly correlates with magnetic properties of MPlCs that can be tuned by changing spatial parameters, such as the period and height of diffraction gratings and thicknesses of functional layers. This work compares the magnetic properties of MPlCs based on Ni80Fe20 (permalloy) obtained from local (longitudinal magneto-optical Kerr effect) and bulk (vibrating-sample magnetometry) measurements and demonstrates an ability to control sensors' performance through changing the magnetic properties of the MPlCs. The influence of the substrate's geometry (planar or sinusoidal and trapezoidal diffraction grating profiles) and the thickness of the surface layer is examined.

{Alzheimer\textquoterights disease (AD) is the most common cause of dementia worldwide. So far, diagnosis of AD is only unequivocally defined through postmortem histology. Amyloid plaques are a classical hallmark of AD and amyloid load is currently quantified by Positron Emission tomography (PET) in vivo. Ultra-high field magnetic resonance imaging (UHF-MRI) can potentially provide a non-invasive biomarker for AD by allowing imaging of pathological processes at a very-high spatial resolution. The first aim of this work was to reproduce the characteristic cortical pattern previously observed in vivo in AD patients using weighted-imaging at 7T. We extended these findings using quantitative susceptibility mapping (QSM) and quantification of the effective transverse relaxation rate (R2\textasteriskcentered) at 9.4T. The second aim was to investigate the origin of the contrast patterns observed in vivo in the cortex of AD patients at 9.4T by comparing quantitative UHF-MRI (9.4T and 14.1T) of postmortem samples with histology. We observed a distinctive cortical pattern in vivo in patients compared to healthy controls (HC), and these findings were confirmed ex vivo. Specifically, we found a close link between the signal changes detected by QSM in the AD sample at 14.1T and the distribution pattern of amyloid plaques in the histological sections of the same specimen. Our findings showed that QSM and R2\textasteriskcentered maps can distinguish AD from HC at UHF by detecting cortical alterations directly related to amyloid plaques in AD patients. Furthermore, we provided a method to quantify amyloid plaque load in AD patients at UHF non-invasively.}