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•Assess the interrelation of brain glucose metabolism & functional connections.•Whole-brain functional input explains glucose metabolism of target region.•Functional input reflects non-oxidative, on-demand metabolism of synaptic signaling.•Shift to higher network integration during task performance.•Decreased segregation of brain networks in the elderly. The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

•Assessment of inter-organ metabolic connectivity.•Automated and manual organ delineation.•Validation of metabolic connectivity approach.•Liver and kidney strongest connectivity with brain. The nervous and circulatory system interconnects the various organs of the human body, building hierarchically organized subsystems, enabling fine-tuned, metabolically expensive brain-body and inter-organ crosstalk to appropriately adapt to internal and external demands. A deviation or failure in the function of a single organ or subsystem could trigger unforeseen biases or dysfunctions of the entire network, leading to maladaptive physiological or psychological responses. Therefore, quantifying these networks in healthy individuals and patients may help further our understanding of complex disorders involving body-brain crosstalk. Here we present a generalized framework to automatically estimate metabolic inter-organ connectivity utilizing whole-body functional positron emission tomography (fPET). The developed framework was applied to 16 healthy subjects (mean age ± SD, 25 ± 6 years; 13 female) that underwent one dynamic 18F-FDG PET/CT scan. Multiple procedures of organ segmentation (manual, automatic, circular volumes) and connectivity estimation (polynomial fitting, spatiotemporal filtering, covariance matrices) were compared to provide an optimized thorough overview of the workflow. The proposed approach was able to estimate the metabolic connectivity patterns within brain regions and organs as well as their interactions. Automated organ delineation, but not simplified circular volumes, showed high agreement with manual delineation. Polynomial fitting yielded similar connectivity as spatiotemporal filtering at the individual subject level. Furthermore, connectivity measures and group-level covariance matrices did not match. The strongest brain-body connectivity was observed for the liver and kidneys. The proposed framework offers novel opportunities towards analyzing metabolic function from a systemic, hierarchical perspective in a multitude of physiological pathological states.

External tasks evoke characteristic fMRI BOLD signal deactivations in the default mode network (DMN). However, for the corresponding metabolic glucose demands both decreases and increases have been reported. To resolve this discrepancy, functional PET/MRI data from 50 healthy subjects performing Tetris were combined with previously published data sets of working memory, visual and motor stimulation. We show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive networks. Specifically, the dorsal attention and frontoparietal network shape the glucose metabolism of the posteromedial DMN in opposing directions. While tasks that mainly require an external focus of attention lead to a consistent downregulation of both metabolism and the BOLD signal in the posteromedial DMN, cognitive control during working memory requires a metabolically expensive BOLD suppression. This indicates that two types of BOLD deactivations with different oxygen-to-glucose index may occur in this region. We further speculate that consistent downregulation of the two signals is mediated by decreased glutamate signaling, while divergence may be subject to active GABAergic inhibition. The results demonstrate that the DMN relates to cognitive processing in a flexible manner and does not always act as a cohesive task-negative network in isolation.

The neurobiological basis of learning is reflected in adaptations of brain structure, network organization and energy metabolism. However, it is still unknown how different neuroplastic mechanisms act together and if cognitive advancements relate to general or task-specific changes. Therefore, we tested how hierarchical network interactions contribute to improvements in the performance of a visuo-spatial processing task by employing simultaneous PET/MR neuroimaging before and after a 4-week learning period. We combined functional PET and metabolic connectivity mapping (MCM) to infer directional interactions across brain regions. Learning altered the top-down regulation of the salience network onto the occipital cortex, with increases in MCM at resting-state and decreases during task execution. Accordingly, a higher divergence between resting-state and task-specific effects was associated with better cognitive performance, indicating that these adaptations are complementary and both required for successful visuo-spatial skill learning. Simulations further showed that changes at resting-state were dependent on glucose metabolism, whereas those during task performance were driven by functional connectivity between salience and visual networks. Referring to previous work, we suggest that learning establishes a metabolically expensive skill engram at rest, whose retrieval serves for efficient task execution by minimizing prediction errors between neuronal representations of brain regions on different hierarchical levels. Brain network analyses reveal coupled changes between functional connectivity and metabolic demands that relate to cognitive performance improvements induced by learning a challenging visuo-spatial task for four weeks.

Determining how deep instruments can be inserted into the femoral canal without touching adjacent structures is a fundamental necessity for navigating instruments in primary and revision total hip arthroplasty. The aim of the study was to determine the reachable depth of a straight instrument inserted into the femur canal during primary and revision total hip arthroplasty. Based on the three-dimensional data of twenty-six femurs, obtained from a CT scan, the insertion depth of a virtual, straight instrument was accessed by a simulation. The effect of the diameter of the virtual instrument and the extension of the osteotomy were evaluated. Without extending the osteotomy, 100% of the femoral canal was reachable to a depth of 5.1-6.3 cm for instruments with a diameter of 10 mm. The depth was measured from the lower edge of the osteotomy. A maximum lateral extension of the osteotomy by 1 cm enlarges the access to a depth of 8.8 cm. The results provide a theoretical basis for the limitations of guiding instruments used for the preparation of the femoral canal. Bone preserving methods need the development of angulated instruments to reach deep areas in the femoral canal.

Purpose Positron emission tomography (PET) provides precise molecular information on physiological processes, but its low temporal resolution is a major obstacle. Consequently, we characterized the metabolic response of the human brain to working memory performance using an optimized functional PET (fPET) framework at a temporal resolution of 3 s. Methods Thirty-five healthy volunteers underwent fPET with [ 18 F]FDG bolus plus constant infusion, 19 of those at a hybrid PET/MRI scanner. During the scan, an n-back working memory paradigm was completed. fPET data were reconstructed to 3 s temporal resolution and processed with a novel sliding window filter to increase signal to noise ratio. BOLD fMRI signals were acquired at 2 s. Results Consistent with simulated kinetic modeling, we observed a constant increase in the [ 18 F]FDG signal during task execution, followed by a rapid return to baseline after stimulation ceased. These task-specific changes were robustly observed in brain regions involved in working memory processing. The simultaneous acquisition of BOLD fMRI revealed that the temporal coupling between hemodynamic and metabolic signals in the primary motor cortex was related to individual behavioral performance during working memory. Furthermore, task-induced BOLD deactivations in the posteromedial default mode network were accompanied by distinct temporal patterns in glucose metabolism, which were dependent on the metabolic demands of the corresponding task-positive networks. Conclusions In sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing. This allows to capture unique information in the temporal domain, which is not accessible to conventional PET imaging.

Bulk high-temperature superconductors (HTSs) such as REBa2Cu3O7−x (REBCO, RE = Y, Gd) are commonly used in rotationally symmetric superconducting magnetic bearings. However, such bulks have several disadvantages such as brittleness, limited availability and high costs due to the time-consuming and energy-intensive fabrication process. Alternatively, tape stacks of HTS-coated conductors might be used for these devices promising an improved bearing efficiency due to a simplification of manufacturing processes for the required shapes, higher mechanical strength, improved thermal performance, higher availability and therefore potentially reduced costs. Hence, tape stacks with a base area of (12 × 12) mm2 and a height of up to 12 mm were prepared and compared to commercial bulks of the same size. The trapped field measurements at 77 K showed slightly higher values for the tape stacks if compared to bulks with the same size. Afterwards, the maximum levitation forces in zero field (ZFC) and field cooling (FC) modes were measured while approaching a permanent magnet, which allows the stiffness in the vertical and lateral directions to be determined. Similar levitation forces were measured in the vertical direction for bulk samples and tape stacks in ZFC and FC modes, whereas the lateral forces were almost zero for stacks with the REBCO films parallel to the magnet. A 90° rotation of the tape stacks with respect to the magnet results in the opposite behavior, i.e., a high lateral but negligible vertical stiffness. This anisotropy originates from the arrangement of decoupled superconducting layers in the tape stacks. Therefore, a combination of stacks with vertical and lateral alignment is required for stable levitation in a bearing.

Purpose Functional positron emission tomography (fPET) with [ 18 F]FDG allows quantification of stimulation-induced changes in glucose metabolism independent of neurovascular coupling. However, the gold standard for quantification requires invasive arterial blood sampling, limiting its widespread use. Here, we introduce a novel fPET method without the need for an input function. Methods We validated the approach using two datasets (DS). For DS1, 52 volunteers (23.2 ± 3.3 years, 24 females) performed Tetris® during a [ 18 F]FDG fPET scan (bolus + constant infusion). For DS2, 18 participants (24.2 ± 4.3 years, 8 females) performed an eyes-open/finger tapping task (constant infusion). Task-specific changes in metabolism were assessed with the general linear model (GLM) and cerebral metabolic rate of glucose (CMRGlu) was quantified with the Patlak plot as reference. We then estimated simplified outcome parameters, including GLM beta values and percent signal change (%SC), and compared them, region and whole-brain-wise. Results We observed higher agreement with the reference for DS1 than DS2. Both DS resulted in strong correlations between regional task-specific beta estimates and CMRGlu ( r  = 0.763…0.912). %SC of beta values exhibited strong agreement with %SC of CMRGlu ( r  = 0.909…0.999). Average activation maps showed a high spatial similarity between CMRGlu and beta estimates (Dice = 0.870…0.979) as well as %SC (Dice = 0.932…0.997), respectively. Conclusion The non-invasive method reliably estimates task-specific changes in glucose metabolism without blood sampling. This streamlines fPET, albeit with the trade-off of being unable to quantify baseline metabolism. The simplification enhances its applicability in research and clinical settings.

Purpose Functional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations. Methods Twenty healthy individuals underwent fPET/MR scans using [ 18 F]FDG or 6-[ 18 F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard. Results For both radiotracer cohorts, moderate to high agreement (r: 0.60–0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87–0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975–0.998) with AIF-derived measurements. Conclusion Our proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.

Recent technological advancements in both instrumentation and analysis methods of human brain imaging data such as electroencephalography (EEG) increasingly allow the measurement of mobile participants that interact with their environment. The new field of Mobile Brain/Body Imaging (MoBI; Gramann et al., 2011; Makeig, Gramann, Jung, Sejnowski, & Poizner, 2009) combines these measurements with imaging methods regarding the body, such as motion or eye tracking, and analyzes the multimodal data in order to investigate natural cognition in action. These analyses require the synchronized import of all data streams, options to process body data modalities, reliable preprocessing of EEG data in light of the elevated amount of non-cortical contributions in mobile settings, and the combined functional analysis of all modalities. Here, especially the sound preprocessing of EEG data from mobile settings suffers from a lack of information regarding a number of parameters that can be adjusted during the cleaning. Additionally, a comprehensive toolbox that addresses all four of these aspects is missing to date, but could be highly beneficial to the field. Two overarching goals were thus formulated in this dissertation: The first is to increase the reliability of MoBI data analysis, on the one hand by investigating the effect of different steps during the EEG processing, and on the other hand by standardizing the analysis of MoBI data. The second is to increase the usability of MoBI data analysis methods, focusing on the employment of easy-to-use and transparent automated processing tools.To realize these goals, this dissertation presents two studies that investigate the parameters mobility, channel density, high-pass filter, and time-domain cleaning on their effect on the decomposition of EEG using independent component analysis (ICA). These studies lead to a set of best practices that can be employed when decomposing EEG data with ICA. Additionally, two automated toolboxes for the analysis of MoBI data are presented: The first, Zapline-plus, allows the removal of frequency specific artifacts from EEG data while minimizing the impact on non-artifactual elements of the data. The second, the BeMoBIL Pipeline, is a comprehensive pipeline for the analysis of MoBI data that addresses the four formulated requirements by making use of Zapline-plus and the information collected in the two studies, and augmenting them with a variety of wrapper functions that can be used automatically with minimal setup time. It emphasizes the use of robust methods to increase the reproducibility of the analysis, and provides documentations of all processing milestones and performed steps. The presented works are finally discussed with regards to their contributions to MoBI as a research method, in particular addressing the use of automation when processing MoBI data. Realizing the two formulated goals, this dissertation seeks to increase the applicability of MoBI in general by consolidating the use of mobile EEG and body data as a highly effective imaging method, and to increase the accessibility of MoBI as a tool for researchers from other fields.