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.

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.

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.

Effects of bilateral sequential theta-burst stimulation on 5-HT1A receptors in the dorsolateral prefrontal cortex in treatment-resistant depression: a proof-of-concept trial.

Theta-burst stimulation (TBS) represents a brain stimulation technique effective for treatment-resistant depression (TRD) as underlined by meta-analyses. While the methodology undergoes constant refinement, bilateral stimulation of the dorsolateral prefrontal cortex (DLPFC) appears promising to restore left DLPFC hypoactivity and right hyperactivity found in depression. The post-synaptic inhibitory serotonin-1A (5-HT1A) receptor, also occurring in the DLPFC, might be involved in this mechanism of action. To test this hypothesis, we performed PET-imaging using the tracer [carbonyl-11C]WAY-100635 including arterial blood sampling before and after a three-week treatment with TBS in 11 TRD patients compared to sham stimulation (n = 8 and n = 3, respectively). Treatment groups were randomly assigned, and TBS protocol consisted of excitatory intermittent TBS to the left and inhibitory continuous TBS to the right DLPFC. A linear mixed model including group, hemisphere, time, and Hamilton Rating Scale for Depression (HAMD) score revealed a 3-way interaction effect of group, time, and HAMD on specific distribution volume (VS) of 5-HT1A receptor. While post-hoc comparisons showed no significant changes of 5-HT1A receptor VS in either group, higher 5-HT1A receptor VS after treatment correlated with greater difference in HAMD (r = -0.62). The results of this proof-of-concept trial hint towards potential effects of TBS on the distribution of the 5-HT1A receptor. Due to the small sample size, all results must, however, be regarded with caution.

Increased left dorsolateral prefrontal cortex density following escitalopram intake during relearning: a randomized, placebo-controlled trial in healthy humans.

Background: Serotonergic agents affect brain plasticity and reverse stress-induced dendritic atrophy in key fronto-limbic brain areas associated with learning and memory. Objectives: The aim of this study was to investigate effects of the antidepressant escitalopram on gray matter during relearning in healthy individuals to inform a model for depression and the neurobiological processes of recovery. Design: Randomized double blind placebo control, monocenter study. Methods: In all, 76 (44 females) healthy individuals performed daily an associative learning task with emotional or non-emotional content over a 3-week period. This was followed by a 3-week relearning period (randomly shuffled association within the content group) with concurrent daily selective serotonin reuptake inhibitor (i.e., 10 mg escitalopram) or placebo intake. Results: Via voxel-based morphometry and only in individuals that developed sufficient escitalopram blood levels over the 21-day relearing period, an increased density of the left dorsolateral prefrontal cortex was found. When investigating whether there was an interaction between relearning and drug intervention for all participants, regardless of escitalopram levels, no changes in gray matter were detected with either surfaced-based or voxel-based morphometry analyses. Conclusion: The left dorsolateral prefrontal cortex affects executive function and emotional processing, and is a critical mediator of symptoms and treatment outcomes of depression. In line, the findings suggest that escitalopram facilitates neuroplastic processes in this region if blood levels are sufficient. Contrary to our hypothesis, an effect of escitalopram on brain structure that is dependent of relearning content was not detected. However, this may have been a consequence of the intensity and duration of the interventions. Registration: ClinicalTrials.gov Identifier: NCT02753738; Trial Name: Enhancement of learning associated neural plasticity by Selective Serotonin Reuptake Inhibitors; URL: https://clinicaltrials.gov/ct2/show/NCT02753738.

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.

First-in-Humans Brain PET Imaging of the GluN2B-Containing N-methyl-d-aspartate Receptor with (R)-11C-Me-NB1.

The N-methyl-d-aspartate receptor (NMDAR) plays a crucial role in neurodegenerative diseases such as Alzheimer disease and in the treatment of major depression by fast-acting antidepressants such as ketamine. Given their broad implications, GluN2B-containing NMDARs have been of interest as diagnostic and therapeutic targets. Recently, (R)-11C-Me-NB1 was investigated preclinically and shown to be a promising radioligand for imaging GluN2B subunits. Here, we report on the performance characteristics of this radioligand in a first-in-humans PET study. Methods: Six healthy male subjects were scanned twice on a fully integrated PET/MR scanner with (R)-11C-Me-NB1 for 120 min. Brain uptake and tracer distribution over time were investigated by SUVs. Test-retest reliability was assessed with the absolute percentage difference and the coefficient of variation. Exploratory total volumes of distribution (VT) were computed using an arterial input function and the Logan plot as well as a constrained 2-tissue-compartment model with the ratio of rate constants between plasma and tissue compartments (K1/k2) coupled (2TCM). SUV was correlated with VT to investigate its potential as a surrogate marker of GluN2B expression. Results: High and heterogeneous radioligand uptake was observed across the entire gray matter with reversible kinetics within the scan time. SUV absolute percentage difference ranged from 6.9% to 8.5% and coefficient of variation from 4.9% to 6.0%, indicating a high test-retest reliability. A moderate correlation was found between SUV averaged from 70 to 90 min and VT using Logan plot (Spearman ρ = 0.44). Correlation between VT Logan and 2TCM was r = 0.76. Conclusion: The radioligand (R)-11C-Me-NB1 was highly effective in mapping GluN2B-enriched NMDARs in the human brain. With a heterogeneous uptake and a high test-retest reliability, this radioligand offers promise to deepen our understanding of the GluN2B-containing NMDAR in the pathophysiology and treatment of neuropsychiatric disease such as Alzheimer disease and major depression. Additionally, it could help in the selection of appropriate doses of GluN2B-targeting drugs.

Comparison and Reliability of Hippocampal Subfield Segmentations Within FreeSurfer Utilizing T1- and T2-Weighted Multispectral MRI Data.

The accurate segmentation of in vivo magnetic resonance imaging (MRI) data is a crucial prerequisite for the reliable assessment of disease progression, patient stratification or the establishment of putative imaging biomarkers. This is especially important for the hippocampal formation, a brain area involved in memory formation and often affected by neurodegenerative or psychiatric diseases. FreeSurfer, a widely used automated segmentation software, offers hippocampal subfield delineation with multiple input options. While a single T1-weighted (T1) sequence is regularly used by most studies, it is also possible and advised to use a high-resolution T2-weighted (T2H) sequence or multispectral information. In this investigation it was determined whether there are differences in volume estimations depending on the input images and which combination of these deliver the most reliable results in each hippocampal subfield. 41 healthy participants (age = 25.2 years ± 4.2 SD) underwent two structural MRIs at three Tesla (time between scans: 23 days ± 11 SD) using three different structural MRI sequences, to test five different input configurations (T1, T2, T2H, T1 and T2, and T1 and T2H). We compared the different processing pipelines in a cross-sectional manner and assessed reliability using test-retest variability (%TRV) and the dice coefficient. Our analyses showed pronounced significant differences and large effect sizes between the processing pipelines in several subfields, such as the molecular layer (head), CA1 (head), hippocampal fissure, CA3 (head and body), fimbria and CA4 (head). The longitudinal analysis revealed that T1 and multispectral analysis (T1 and T2H) showed overall higher reliability across all subfields than T2H alone. However, the specific subfields had a substantial influence on the performance of segmentation results, regardless of the processing pipeline. Although T1 showed good test-retest metrics, results must be interpreted with caution, as a standard T1 sequence relies heavily on prior information of the atlas and does not take the actual fine structures of the hippocampus into account. For the most accurate segmentation, we advise the use of multispectral information by using a combination of T1 and high-resolution T2-weighted sequences or a T2 high-resolution sequence alone.

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.

Functional dynamics of dopamine synthesis during monetary reward and punishment processing.

The assessment of dopamine release with the PET competition model is thoroughly validated but entails disadvantages for the investigation of cognitive processes. We introduce a novel approach incorporating 6-[18F]FDOPA uptake as index of the dynamic regulation of dopamine synthesis enzymes by neuronal firing. The feasibility of this approach is demonstrated by assessing widely described sex differences in dopamine neurotransmission. Reward processing was behaviorally investigated in 36 healthy participants, of whom 16 completed fPET and fMRI during the monetary incentive delay task. A single 50 min fPET acquisition with 6-[18F]FDOPA served to quantify task-specific changes in dopamine synthesis. In men monetary gain induced stronger increases in ventral striatum dopamine synthesis than loss. Interestingly, the opposite effect was discovered in women. These changes were further associated with reward (men) and punishment sensitivity (women). As expected, fMRI showed robust task-specific neuronal activation but no sex difference. Our findings provide a neurobiological basis for known behavioral sex differences in reward and punishment processing, with important implications in psychiatric disorders showing sex-specific prevalence, altered reward processing and dopamine signaling. The high temporal resolution and magnitude of task-specific changes make fPET a promising tool to investigate functional neurotransmitter dynamics during cognitive processing and in brain disorders.

Reconfiguration of functional brain networks and metabolic cost converge during task performance.

The ability to solve cognitive tasks depends upon adaptive changes in the organization of whole-brain functional networks. However, the link between task-induced network reconfigurations and their underlying energy demands is poorly understood. We address this by multimodal network analyses integrating functional and molecular neuroimaging acquired concurrently during a complex cognitive task. Task engagement elicited a marked increase in the association between glucose consumption and functional brain network reorganization. This convergence between metabolic and neural processes was specific to feedforward connections linking the visual and dorsal attention networks, in accordance with task requirements of visuo-spatial reasoning. Further increases in cognitive load above initial task engagement did not affect the relationship between metabolism and network reorganization but only modulated existing interactions. Our findings show how the upregulation of key computational mechanisms to support cognitive performance unveils the complex, interdependent changes in neural metabolism and neuro-vascular responses.

Topologically Guided Prioritization of Candidate Gene Transcripts Coexpressed with the 5-HT1A Receptor by Combining In Vivo PET and Allen Human Brain Atlas Data.

The serotonin-1A receptor (5-HT1AR) represents a viable target in the treatment of disorders of the brain. However, development of psychiatric drugs continues to be hindered by the relative inaccessibility of brain tissue. Although the efficacy of drugs selective for the 5-HT1AR has not been proven, research continues to focus on drugs that influence this receptor subtype. To further knowledge on this topic, we investigated the topological coexpression patterns of the 5-HT1AR. We calculated Spearman's rho for the correlation of positron emission tomography-binding potentials (BPND) of the 5-HT1AR assessed in 30 healthy subjects using the tracer [carbonyl-11C]WAY-100635 and predicted whole-brain mRNA expression of 18 686 genes. After applying a threshold of r > 0.3 in a leave-one-out cross-validation of the prediction of mRNA expression, genes with ρ ≥ 0.7 were considered to be relevant. In cortical regions, 199 genes showed high correlation with the BPND of the 5-HT1AR, in subcortical regions 194 genes. Using our approach, we could consolidate the role of BDNF and implicate new genes (AnxA8, NeuroD2) in serotonergic functioning. Despite its explorative nature, the analysis can be seen as a gene prioritization approach to reduce the number of genes potentially connected to 5-HT1AR functioning and guide future in vitro studies.

Serotonin Transporter Binding in the Human Brain After Pharmacological Challenge Measured Using PET and PET/MR.

Introduction: In-vivo quantification of the serotonin transporter (SERT) guided our understanding of many neuropsychiatric disorders. A recently introduced bolus plus constant infusion protocol has been shown to allow the reliable determination of SERT binding with reduced scan time. In this work, the outcomes of two methods, a bolus injection paradigm on a GE PET camera, and a bolus plus infusion paradigm on a combined Siemens PET/MR camera were compared. Methods: A total of seven healthy subjects underwent paired PET and paired PET/MR scans each with intravenous double-blind application of 7.5 mg citalopram or saline in a randomized cross-over study design. While PET scans were performed according to standard protocols and non-displaceable binding potentials (BPND) were calculated using the multi-linear reference tissue model, during PET/MR measurements [11C]DASB was applied as bolus plus constant infusion, and BPND was calculated using the steady state method and data acquired at tracer equilibrium. Occupancies were calculated as the relative decrease in BPND between saline and citalopram scans. Results: During placebo scans, a mean difference in BPND of -0.08 (-11.71%) across all ROIs was found between methods. PET/MR scans resulted in higher BPND estimates than PET scans in all ROIs except the midbrain. A mean difference of -0.19 (-109.40%) across all ROIs between methods was observed for citalopram scans. PET/MR scans resulted in higher BPND estimates than PET scans in all ROIs. For occupancy, a mean difference of 23.12% (21.91%) was observed across all ROIs. PET/MR scans resulted in lower occupancy compared to PET scans in all ROIs except the temporal cortex. While for placebo, BPND of high-binding regions (thalamus and striatum) exhibited moderate reliability (ICC = 0.66), during citalopram scans ICC decreased (0.36-0.46). However, reliability for occupancy remained high (0.57-0.82). Conclusion: Here, we demonstrated the feasibility of reliable and non-invasive SERT quantification using a [11C]DASB bolus plus constant infusion protocol at a hybrid PET/MR scanner, which might facilitate future pharmacological imaging studies. Highest agreement with established methods for quantification of occupancy and SERT BPND at baseline was observed in subcortical high-binding regions.

Parcellation of the Human Cerebral Cortex Based on Molecular Targets in the Serotonin System Quantified by Positron Emission Tomography In vivo.

Parcellation of distinct areas in the cerebral cortex has a long history in neuroscience and is of great value for the study of brain function, specialization, and alterations in neuropsychiatric disorders. Analysis of cytoarchitectonical features has revealed their close association with molecular profiles based on protein density. This provides a rationale for the use of in vivo molecular imaging data for parcellation of the cortex with the advantage of whole-brain coverage. In the current work, parcellation was based on expression of key players of the serotonin neurotransmitter system. Positron emission tomography was carried out for the quantification of serotonin 1A (5-HT1A, n = 30) and 5-HT2A receptors (n = 22), the serotonin-degrading enzyme monoamine oxidase A (MAO-A, n = 32) and the serotonin transporter (5-HTT, n = 24) in healthy participants. Cortical protein distribution maps were obtained using surface-based quantification. Based on k-means clustering, silhouette criterion and bootstrapping, five distinct clusters were identified as the optimal solution. The defined clusters proved of high explanatory value for the effects of psychotropic drugs acting on the serotonin system, such as antidepressants and psychedelics. Therefore, the proposed method constitutes a sensible approach towards integration of multimodal imaging data for research and development in neuropharmacology and psychiatry.