Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Validation of cardiac image-derived input functions for functional PET quantification.

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 [18F]FDG or 6-[18F]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.

Non-invasive assessment of stimulation-specific changes in cerebral glucose metabolism with functional PET.

PURPOSE: Functional positron emission tomography (fPET) with [18F]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 [18F]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.

High-temporal resolution functional PET/MRI reveals coupling between human metabolic and hemodynamic brain response.

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 [18F]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 [18F]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.

Links between Exercise Capacity, Exercise Training, and Metabolism.

Exercise capacity of an individual describes the ability to perform physical activity. This exercise capacity is influenced by intrinsic factors such as genetic constitution and extrinsic factors such as exercise training. On the metabolic level exercise and metabolism are linked. As an important site of metabolism and the main source for ATP needed for muscle contraction, mitochondrial function can determine exercise capacity, and exercise inversely influences mitochondrial function. It has been suggested that exercise mediates many of its effects due to such metabolic changes. Although extrinsic factors affect exercise capacity, a major part of an individual's exercise capacity is genetically determined, and extrinsic factors can only improve on this baseline. Looking at the effect of exercise capacity on and with disease, the two go hand in hand. On one hand, disease is negatively affecting an individual's exercise capacity; on the other hand, exercise offers an effective treatment option. Combining these factors, exercise capacity is an often-ignored prognostic variable for life expectancy as well as morbidity and mortality. In this review, we aim to provide the current knowledge on the links between inherited and acquired exercise capacity, as well as the mechanisms in which metabolism interacts with exercise capacity. © 2023 American Physiological Society. Compr Physiol 13:5115-5155, 2023.

Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks.

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.

Whole-body metabolic connectivity framework with functional PET.

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.

Learning induces coordinated neuronal plasticity of metabolic demands and functional brain networks.

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.

Reliability of task-specific neuronal activation assessed with functional PET, ASL and BOLD imaging.

Mapping the neuronal response during cognitive processing is of crucial importance to gain new insights into human brain function. BOLD imaging and ASL are established MRI methods in this endeavor. Recently, the novel approach of functional PET (fPET) was introduced, enabling absolute quantification of glucose metabolism at rest and during task execution in a single measurement. Here, we report test-retest reliability of fPET in direct comparison to BOLD imaging and ASL. Twenty healthy subjects underwent two PET/MRI measurements, providing estimates of glucose metabolism, cerebral blood flow (CBF) and blood oxygenation. A cognitive task was employed with different levels of difficulty requiring visual-motor coordination. Task-specific neuronal activation was robustly detected with all three imaging approaches. The highest reliability was obtained for glucose metabolism at rest. Although this dropped during task performance it was still comparable to that of CBF. In contrast, BOLD imaging yielded high performance only for qualitative spatial overlap of task effects but not for quantitative comparison. Hence, the combined assessment of fPET and ASL offers reliable and simultaneous absolute quantification of glucose metabolism and CBF at rest and task.

How deep can straight instruments be inserted into the femoral canal: a simulation study based on cadaveric femora.

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.

Retracting Soft Tissue in Minimally Invasive Hip Arthroplasty Using a Robotic Arm: A Comparison Between a Semiactive Retractor Holder and Human Assistants in a Cadaver Study.

BACKGROUND: All surgical procedures in orthopedics involve the retraction of soft tissue. In this study, the performance of 3 assistants holding the medial retractor during minimally invasive hip arthroplasty was compared with a semiactive retractor holder in a cadaver setup. METHODS: A total of 40 measurements on 3 cadavers were carried out with each subject (3 human, 1 robot) measuring each cadaver 10 times. The retractor was equipped with a sensor array on both sides, to measure variations of the retracting pressures over a 2-minute interval. RESULTS: The semiactive retractor holder showed an almost constant performance compared with the test subjects. There was no significant reduction of the applied pressure and almost no variation during the 2-minute interval and across all measurements. CONCLUSIONS: The performance of the semiactive retractor holder was more stable than that of a human assistant, making it suitable for intraoperative usage.

Assessment of the Size of the Surgical Site in Minimally Invasive Hip Surgery.

Objective: Minimally invasive approaches to the hip are beneficial to the patient, but reduce the space available for manipulation by the surgeon. Determining the available working space is important for the development of surgical instruments, to track movements during surgery, as well as to classify the invasiveness of the procedure. Approach: We evaluate three measurement methods to assess the volume of eight surgical sites in a cadaver study. The cavities were filled with an alginate cast to determine its dimensions. Second, the depth, height, and width of the surgical site were measured with a ruler and the volume was calculated. Last, the surface registration method was used to reconstruct the site. Results: We found that the mold filling method provides accurate results in determining the volume of a surgical site. The manual method using a ruler showed excellent reliability, but the calculations tended to overestimate the volume of the surgical site. In contrast, surface reconstruction tended to underestimate the volume of a surgical site, but the results closer resembled the ones derived from the mold filling method. Innovation: We presented a new method to assess the size of the surgical site intraoperatively in minimally invasive hip surgery. Conclusion: The manual method is reliable, but not as accurate as the surface reconstruction, while the mold filling method cannot be used in an intraoperative setup. Although surface reconstruction showed deficits regarding reliability, due to the lack of direct contact to the patient, it remains an appealing technique to measure the surgical site.

The use of time-of-flight camera for navigating robots in computer-aided surgery: monitoring the soft tissue envelope of minimally invasive hip approach in a cadaver study.

BACKGROUND: Time-of-flight (TOF) cameras can guide surgical robots or provide soft tissue information for augmented reality in the medical field. In this study, a method to automatically track the soft tissue envelope of a minimally invasive hip approach in a cadaver study is described. METHODS: An algorithm for the TOF camera was developed and 30 measurements on 8 surgical situs (direct anterior approach) were carried out. The results were compared to a manual measurement of the soft tissue envelope. RESULTS: The TOF camera showed an overall recognition rate of the soft tissue envelope of 75%. On comparing the results from the algorithm with the manual measurements, a significant difference was found (P > .005). CONCLUSIONS: In this preliminary study, we have presented a method for automatically recognizing the soft tissue envelope of the surgical field in a real-time application. Further improvements could result in a robotic navigation device for minimally invasive hip surgery.