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.

Whole-brain deuterium metabolic imaging via concentric ring trajectory readout enables assessment of regional variations in neuronal glucose metabolism.

Deuterium metabolic imaging (DMI) is an emerging magnetic resonance technique, for non-invasive mapping of human brain glucose metabolism following oral or intravenous administration of deuterium-labeled glucose. Regional differences in glucose metabolism can be observed in various brain pathologies, such as Alzheimer's disease, cancer, epilepsy or schizophrenia, but the achievable spatial resolution of conventional phase-encoded DMI methods is limited due to prolonged acquisition times rendering submilliliter isotropic spatial resolution for dynamic whole brain DMI not feasible. The purpose of this study was to implement non-Cartesian spatial-spectral sampling schemes for whole-brain 2H FID-MR Spectroscopic Imaging to assess time-resolved metabolic maps with sufficient spatial resolution to reliably detect metabolic differences between healthy gray and white matter regions. Results were compared with lower-resolution DMI maps, conventionally acquired within the same session. Six healthy volunteers (4 m/2 f) were scanned for ~90 min after administration of 0.8 g/kg oral [6,6']-2H glucose. Time-resolved whole brain 2H FID-DMI maps of glucose (Glc) and glutamate + glutamine (Glx) were acquired with 0.75 and 2 mL isotropic spatial resolution using density-weighted concentric ring trajectory (CRT) and conventional phase encoding (PE) readout, respectively, at 7 T. To minimize the effect of decreased signal-to-noise ratios associated with smaller voxels, low-rank denoising of the spatiotemporal data was performed during reconstruction. Sixty-three minutes after oral tracer uptake three-dimensional (3D) CRT-DMI maps featured 19% higher (p = .006) deuterium-labeled Glc concentrations in GM (1.98 ± 0.43 mM) compared with WM (1.66 ± 0.36 mM) dominated regions, across all volunteers. Similarly, 48% higher (p = .01) 2H-Glx concentrations were observed in GM (2.21 ± 0.44 mM) compared with WM (1.49 ± 0.20 mM). Low-resolution PE-DMI maps acquired 70 min after tracer uptake featured smaller regional differences between GM- and WM-dominated areas for 2H-Glc concentrations with 2.00 ± 0.35 mM and 1.71 ± 0.31 mM, respectively (+16%; p = .045), while no regional differences were observed for 2H-Glx concentrations. In this study, we successfully implemented 3D FID-MRSI with fast CRT encoding for dynamic whole-brain DMI at 7 T with 2.5-fold increased spatial resolution compared with conventional whole-brain phase encoded (PE) DMI to visualize regional metabolic differences. The faster metabolic activity represented by 48% higher Glx concentrations was observed in GM- compared with WM-dominated regions, which could not be reproduced using whole-brain DMI with the low spatial resolution protocol. Improved assessment of regional pathologic alterations using a fully non-invasive imaging method is of high clinical relevance and could push DMI one step toward clinical applications.

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.

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.

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.

Gender Dysphoria and Sexual Euphoria: A Bayesian Perspective on the Influence of Gender-Affirming Hormone Therapy on Sexual Arousal.

Self-reported sexual orientation of transgender individuals occasionally changes over transition. Using functional magnetic resonance imaging, we tested the hypothesis that neural and behavioral patterns of sexual arousal in transgender individuals would shift from the assigned to the experienced gender (e.g., trans women's responses becoming more dissimilar to those of cis men and more similar to those of cis women). To this aim, trans women (N = 12) and trans men (N = 20) as well as cisgender women (N = 24) and cisgender men (N = 14) rated visual stimuli showing male-female, female-female or male-male intercourse for sexual arousal before and after four months of gender-affirming hormone therapy. A Bayesian framework allowed us to incorporate previous behavioral findings. The hypothesized changes could indeed be observed in the behavioral responses with the strongest results for trans men and female-female scenes. Activation of the ventral striatum supported our hypothesis only for female-female scenes in trans women. The respective application or depletion of androgens in trans men and trans women might partly explain this observation. The prominent role of female-female stimuli might be based on the differential responses they elicit in cis women and men or, in theory, the controversial concept of autogynephilia. We show that correlates of sexual arousal in transgender individuals might change in the direction of the experienced gender. Future investigations should elucidate the mechanistic role of sex hormones and the cause of the differential neural and behavioral findings.The study was registered at ClinicalTrials.gov (NCT02715232), March 22, 2016.

Dissociations between glucose metabolism and blood oxygenation in the human default mode network revealed by simultaneous PET-fMRI.

The finding of reduced functional MRI (fMRI) activity in the default mode network (DMN) during externally focused cognitive control has been highly influential to our understanding of human brain function. However, these negative fMRI responses, measured as relative decreases in the blood-oxygenation-level-dependent (BOLD) response between rest and task, have also prompted major questions of interpretation. Using hybrid functional positron emission tomography (PET)-MRI, this study shows that task-positive and -negative BOLD responses do not reflect antagonistic patterns of synaptic metabolism. Task-positive BOLD responses in attention and control networks were accompanied by concomitant increases in glucose metabolism during cognitive control, but metabolism in widespread DMN remained high during rest and task despite negative BOLD responses. Dissociations between glucose metabolism and the BOLD response specific to the DMN reveal functional heterogeneity in this network and demonstrate that negative BOLD responses during cognitive control should not be interpreted to reflect relative increases in metabolic activity during rest. Rather, neurovascular coupling underlying BOLD response patterns during rest and task in DMN appears fundamentally different from BOLD responses in other association networks during cognitive control.

Reproducibility of 3D MRSI for imaging human brain glucose metabolism using direct (2H) and indirect (1H) detection of deuterium labeled compounds at 7T and clinical 3T.

INTRODUCTION: Deuterium metabolic imaging (DMI) and quantitative exchange label turnover (QELT) are novel MR spectroscopy techniques for non-invasive imaging of human brain glucose and neurotransmitter metabolism with high clinical potential. Following oral or intravenous administration of non-ionizing [6,6'-2H2]-glucose, its uptake and synthesis of downstream metabolites can be mapped via direct or indirect detection of deuterium resonances using 2H MRSI (DMI) and 1H MRSI (QELT), respectively. The purpose of this study was to compare the dynamics of spatially resolved brain glucose metabolism, i.e., estimated concentration enrichment of deuterium labeled Glx (glutamate+glutamine) and Glc (glucose) acquired repeatedly in the same cohort of subjects using DMI at 7T and QELT at clinical 3T. METHODS: Five volunteers (4 m/1f) were scanned in repeated sessions for 60 min after overnight fasting and 0.8 g/kg oral [6,6'-2H2]-glucose administration using time-resolved 3D 2H FID-MRSI with elliptical phase encoding at 7T and 3D 1H FID-MRSI with a non-Cartesian concentric ring trajectory readout at clinical 3T. RESULTS: One hour after oral tracer administration regionally averaged deuterium labeled Glx4 concentrations and the dynamics were not significantly different over all participants between 7T 2H DMI and 3T 1H QELT data for GM (1.29±0.15 vs. 1.38±0.26 mM, p=0.65 & 21±3 vs. 26±3 µM/min, p=0.22) and WM (1.10±0.13 vs. 0.91±0.24 mM, p=0.34 & 19±2 vs. 17±3 µM/min, p=0.48). Also, the observed time constants of dynamic Glc6 data in GM (24±14 vs. 19±7 min, p=0.65) and WM (28±19 vs. 18±9 min, p=0.43) dominated regions showed no significant differences. Between individual 2H and 1H data points a weak to moderate negative correlation was observed for Glx4 concentrations in GM (r=-0.52, p<0.001), and WM (r=-0.3, p<0.001) dominated regions, while a strong negative correlation was observed for Glc6 data GM (r=-0.61, p<0.001) and WM (r=-0.70, p<0.001). CONCLUSION: This study demonstrates that indirect detection of deuterium labeled compounds using 1H QELT MRSI at widely available clinical 3T without additional hardware is able to reproduce absolute concentration estimates of downstream glucose metabolites and the dynamics of glucose uptake compared to 2H DMI data acquired at 7T. This suggests significant potential for widespread application in clinical settings especially in environments with limited access to ultra-high field scanners and dedicated RF hardware.

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.

Effect of MAOA DNA Methylation on Human in Vivo Protein Expression Measured by [11C]harmine Positron Emission Tomography.

BACKGROUND: Epigenetic modifications like DNA methylation are understood as an intermediary between environmental factors and neurobiology. Cerebral monoamine oxidase A (MAO-A) levels are altered in depression, as are DNA methylation levels within the MAOA gene, particularly in the promoter/exon I/intron I region. An effect of MAOA methylation on peripheral protein expression was shown, but the extent to which methylation affects brain MAO-A levels is not fully understood. METHODS: Here, the influence of MAOA promoter/exon I/intron I region DNA methylation on global MAO-A distribution volume (VT), an index of MAO-A density, was assessed via [11C]harmine positron emission tomography in 22 patients (14 females) suffering from seasonal affective disorder and 30 healthy controls (17 females). RESULTS: No significant influence of MAOA DNA methylation on global MAO-A VT was found, despite correction for health status, sex, season, and MAOA variable number of tandem repeat genotype. However, season affected average methylation in women, with higher levels in spring and summer (Puncorr = .03). We thus did not find evidence for an effect of MAOA DNA methylation on brain MAO-A VT. CONCLUSIONS: In contrast to a previous study demonstrating an effect of methylation of a MAOA promoter region located further 5' on brain MAO-A, MAOA methylation of the region assessed here appears to affect brain protein levels to a limited extent at most. The observed effect of season on methylation levels is in accordance with extensive evidence for seasonal effects within the serotonergic system. CLINICALTRIALS.GOV IDENTIFIER: NCT02582398 (https://clinicaltrials.gov/ct2/show/NCT02582398).

ABCB1 variants and sex affect serotonin transporter occupancy in the brain.

Strategies to personalize psychopharmacological treatment promise to improve efficacy and tolerability. We measured serotonin transporter occupancy immediately after infusion of the widely prescribed P-glycoprotein substrate citalopram and assessed to what extent variants of the ABCB1 gene affect drug target engagement in the brain in vivo. A total of 79 participants (39 female) including 31 patients with major depression and 48 healthy volunteers underwent two PET/MRI scans with the tracer [11C]DASB and placebo-controlled infusion of citalopram (8 mg) in a cross-over design. We tested the effect of six ABCB1 single nucleotide polymorphisms and found lower SERT occupancy in ABCB1 rs2235015 minor allele carriers (n = 26, MAF = 0.18) compared to major allele homozygotes (t73 = 2.73, pFWE < 0.05) as well as in men compared to women (t73 = 3.33, pFWE < 0.05). These effects were robust to correction for citalopram plasma concentration, age and diagnosis. From occupancy we derived the ratio of occupied to unoccupied SERT, because in theory this measure is equal to the product of drug affinity and concentration at target sites. A model combining genotype with basic clinical variables, predicted that, at the same dosage, occupied to unoccupied SERT ratio was -14.48 ± 5.38% lower in rs2235015 minor allele carriers, +19.10 ± 6.95% higher in women, -4.83 ± 2.70% lower per 10 kg bodyweight, and -2.68 ± 3.07% lower per 10 years of age. Our results support the exploration of clinical algorithms with adjustment of initial citalopram dosing and highlight the potential of imaging-genetics for precision pharmacotherapy in psychiatry.

Changes to hypothalamic volume and associated subunits during gender-affirming hormone therapy.

BACKGROUND: Among its pleiotropic properties, gender-affirming hormone therapy (GHT) affects regional brain volumes. The hypothalamus, which regulates neuroendocrine function and associated emotional and cognitive processes, is an intuitive target for probing GHT effects. We sought to assess changes to hypothalamus and hypothalamic subunit volumes after GHT, thereby honouring the region's anatomical and functional heterogeneity. METHODS: Individuals with gender dysphoria and cisgender controls underwent 2 MRI measurements, with a median interval of 145 days (interquartile range [IQR] 128.25-169.75 d, mean 164.94 d) between the first and second MRI. Transgender women (TW) and transgender men (TM) underwent the first MRI before GHT and the second MRI after approximately 4.5 months of GHT, which comprised estrogen and anti-androgen therapy in TW or testosterone therapy in TM. Hypothalamic volumes were segmented using FreeSurfer, and effects of GHT were tested using repeated-measures analysis of covariance. RESULTS: The final sample included 106 participants: 38 TM, 15 TW, 32 cisgender women (CW) and 21 cisgender men (CM). Our analyses revealed group × time interaction effects for total, left and right hypothalamus volume, and for several subunits (left and right inferior tubular, left superior tubular, right anterior inferior, right anterior superior, all p corr < 0.01). In TW, volumes decreased between the first and second MRI in these regions (all p corr ≤ 0.01), and the change from the first to second MRI in TW differed significantly from that in CM and CW in several subunits (p corr < 0.05). LIMITATIONS: We did not address the influence of transition-related psychological and behavioural changes. CONCLUSION: Our results suggest a subunit-specific effect of GHT on hypothalamus volumes in TW. This finding is in accordance with previous reports of positive and negative effects of androgens and estrogens, respectively, on cerebral volumes.

Correlation of receptor density and mRNA expression patterns in the human cerebral cortex.

Changes in distribution of associated molecular targets have been reported across several neuropsychiatric disorders. However, the high-resolution topology of most proteins is unknown and simultaneous in vivo measurement in multi-receptor systems is complicated. To account for the missing proteomic information, messenger ribonucleic acid (mRNA) transcripts are typically used as a surrogate. Nonetheless, post-transcriptional and post-translational processes might cause the discrepancy between the final distribution of proteins and gene expression patterns. Therefore, this study aims to investigate ex vivo links between mRNA expression and corresponding receptor density in the human cerebral cortex. To this end, autoradiography data on the density of 15 different receptors in 38 brain regions were correlated with the expression patterns of 50 associated genes derived from microarray data (mA), RNA sequencing data (RNA-Seq) provided by the Allen Human Brain Atlas and predicted mRNA expression patterns (pred-mRNA). Spearman's rank correlation was used to evaluate the possible links between proteomic data and mRNA expression patterns. Correlations between mRNA and protein density varied greatly between targets: Positive associations were found for e.g. the serotonin 1A (pred-mRNA: rs = 0.708; mA: rs = 0.601) or kainate receptor (pred-mRNA: rs = 0.655; mA: rs = 0.601; RNA-Seq: rs = 0.575) as well as a few negative associations e.g. γ-Aminobutyric acid (GABA) A receptor subunit α3 (pred-mRNA: rs = -0.638; mA: rs = -0.619) or subunit α5 (pred-mRNA: rs = -0.565; mA: rs = -0.563), while most of the other investigated target receptors showed low correlations. The high variability in the correspondence of mRNA expression and receptor spatial distribution warrants caution when inferring the topology of molecular targets in the brain from transcriptome data. This not only highlights the longstanding value of molecular imaging but also indicates a need for comprehensive proteomic studies.

Serotonergic modulation of effective connectivity in an associative relearning network during task and rest.

An essential core function of one's cognitive flexibility is the use of acquired knowledge and skills to adapt to ongoing environmental changes. Animal models have highlighted the influence serotonin has on neuroplasticity. These effects have been predominantly demonstrated during emotional relearning which is theorized as a possible model for depression. However, translation of these mechanisms is in its infancy. To this end, we assessed changes in effective connectivity at rest and during associative learning as a proxy of neuroplastic changes in healthy volunteers. 76 participants underwent 6 weeks of emotional or non-emotional (re)learning (face-matching or Chinese character-German noun matching). During relearning participants either self-administered 10 mg/day of the selective serotonin reuptake inhibitor (SSRI) escitalopram or placebo in a double-blind design. Associative learning tasks, resting-state and structural images were recorded before and after both learning phases (day 1, 21 and 42). Escitalopram intake modulated relearning changes in a network encompassing the right insula, anterior cingulate cortex and right angular gyrus. Here, the process of relearning during SSRI intake showed a greater decrease in effective connectivity from the right insula to both the anterior cingulate cortex and right angular gyrus, with increases in the opposite direction when compared to placebo. In contrast, intrinsic connections and those at resting-state were only marginally affected by escitalopram. Further investigation of gray matter volume changes in these functionally active regions revealed no significant SSRI-induced structural changes. These findings indicate that the right insula plays a central role in the process of relearning and SSRIs further potentiate this effect. In sum, we demonstrated that SSRIs amplify learning-induced effective connections rather than affecting the intrinsic task connectivity or that of resting-state.

Escitalopram modulates learning content-specific neuroplasticity of functional brain networks.

Learning-induced neuroplastic changes, further modulated by content and setting, are mirrored in brain functional connectivity (FC). In animal models, selective serotonin reuptake inhibitors (SSRIs) have been shown to facilitate neuroplasticity. This is especially prominent during emotional relearning, such as fear extinction, which may translate to clinical improvements in patients. To investigate a comparable modulation of neuroplasticity in humans, 99 healthy subjects underwent three weeks of emotional (matching faces) or non-emotional learning (matching Chinese characters to unrelated German nouns). Shuffled pairings of the original content were subsequently relearned for the same time. During relearning, subjects received either a daily dose of the SSRI escitalopram or placebo. Resting-state functional magnetic resonance imaging was performed before and after the (re-)learning phases. FC changes in a network comprising Broca's area, the medial prefrontal cortex, the right inferior temporal and left lingual gyrus were modulated by escitalopram intake. More specifically, it increased the bidirectional connectivity between medial prefrontal cortex and lingual gyrus for non-emotional and the connectivity from medial prefrontal cortex to Broca's area for emotional relearning. The context dependence of these effects together with behavioral correlations supports the assumption that SSRIs in clinical practice improve neuroplasticity rather than psychiatric symptoms per se. Beyond expanding the complexities of learning, these findings emphasize the influence of external factors on human neuroplasticity.

Neuroimaging the menstrual cycle: A multimodal systematic review.

Increasing evidence indicates that ovarian hormones affect brain structure, chemistry and function of women in their reproductive age, potentially shaping their behavior and mental health. Throughout the reproductive years, estrogens and progesterone levels fluctuate across the menstrual cycle and can modulate neural circuits involved in affective and cognitive processes. Here, we review seventy-seven neuroimaging studies and provide a comprehensive and data-driven evaluation of the accumulating evidence on brain plasticity associated with endogenous ovarian hormone fluctuations in naturally cycling women (n = 1304). The results particularly suggest modulatory effects of ovarian hormones fluctuations on the reactivity and structure of cortico-limbic brain regions. These findings highlight the importance of performing multimodal neuroimaging studies on neural correlates of systematic ovarian hormone fluctuations in naturally cycling women based on careful menstrual cycle staging.

Regional gene expression patterns are associated with task-specific brain activation during reward and emotion processing measured with functional MRI.

The exploration of the spatial relationship between gene expression profiles and task-evoked response patterns known to be altered in neuropsychiatric disorders, for example depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in cellular localization processes, metabolism, translation, and synapse regulation. An overlap of these genes with risk genes from major depressive disorder genome-wide association studies revealed the involvement of the master regulators TCF4 and PAX6 in emotion and reward processing. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.

White matter microstructure and volume correlates of premenstrual dysphoric disorder.

BACKGROUND: Premenstrual dysphoric disorder (PMDD) is a mood disorder characterized by psychological and physical symptoms. Differences in white matter have been associated with affective and anxiety disorders, which share some symptoms with PMDD. However, whether white matter structure differs between the brains of individuals with PMDD and healthy controls is not known, nor is its relation to symptom severity. METHODS: We performed tract-based spatial statistics and voxel-based morphometry analyses of diffusion tensor imaging metrics and white matter volume, using 2 neuroimaging data sets (n = 67 and n = 131) and a combined whole-brain and region-of-interest approach. We performed correlation analyses to investigate the relationship between regions with different white matter microstructure and volume and PMDD symptom severity. RESULTS: We found greater fractional anisotropy in the left uncinate fasciculus (d = 0.69) in individuals with PMDD compared to controls. Moreover, the volume of the right uncinate fasciculus was higher in individuals with PMDD compared to controls (d = 0.40). As well, the severity of premenstrual depression was positively correlated with fractional anisotropy in the right superior longitudinal fasciculus (r = 0.35). LIMITATIONS: It is challenging to interpret group differences in diffusion tensor imaging metrics in terms of their underlying biophysical properties. The small size of the control group in the diffusion tensor imaging study may have prevented effects of interest from being detected. CONCLUSION: The findings of the present study provide evidence of differential cerebral white matter structure associated with PMDD and its symptoms.

Assessment of cerebral glucose metabolism in patients with heart failure by 18F-FDG PET/CT imaging.

BACKGROUND: To evaluate the cerebral metabolism in patients with heart failure (HF). METHODS: One hundred and two HF patients were prospectively enrolled, who underwent gated 99mTc-sestamibi single photon emission computed tomography (SPECT)/CT, cardiac and cerebral 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT. Fifteen healthy volunteers served as controls. Patients were stratified by extent of hibernating myocardium (HM) and left ventricular ejection fraction (LVEF) into 4 groups where Group1: HM < 10% (n = 33); Group2: HM ≥ 10%, LVEF < 25% (n = 34); Group3: HM ≥ 10%, 25% ≤ LVEF ≤ 40% (n = 16) and Group 4: LVEF > 40% (n = 19). The standardized uptake value (SUV) in the whole brain (SUVwhole-brain) and the SUV ratios (SUVR) in 24 cognition-related brain regions were determined. SUVwhole-brain and SUVRs were compared between the 4 patient groups and the healthy controls. RESULTS: SUVwhole-brain (r = 0.245, P = 0.013) and SUVRs in frontal areas, hippocampus, and para-hippocampus (r: 0.213 to 0.308, all P < 0.05) were correlated with HM. SUVwhole-brain differed between four patient groups and the healthy volunteers (P = 0.016) and SUVwhole-brain in Group 1 was lower than that in healthy volunteers (P < 0.05). SUVRs of Group 3 in frontal areas were the highest among four patient subgroups (P < 0.05). CONCLUSIONS: Cerebral metabolism in the whole brain was reduced but maintained in cognition-related frontal areas in HF patients with HM and moderately impaired global left ventricular function.

Neuroimaging premenstrual dysphoric disorder: A systematic and critical review.

Endocrine organizational and activational influences on cognitive and affective circuits are likely critical to the development of premenstrual dysphoric disorder (PMDD), a sex-specific hormone-dependent mood disorder. An overview of the anatomical and functional neural characterization of this disorder is presented here by means of neuroimaging correlates, identified from eighteen publications (n = 361 subjects). While white matter integrity remains uninvestigated, greater cerebellar grey matter volume and metabolism were observed in patients with PMDD, along with altered serotonergic and GABAergic neurotransmission. Differential corticolimbic activation in response to emotional stimuli distinguishes the PMDD brain, namely enhanced amygdalar and diminished fronto-cortical function. Thus far, the emotional distress and dysregulation linked to PMDD seem to be defined by structural, chemical and functional brain signatures; however, their characterization remains sparsely studied and somewhat inconsistent. Clear and well-replicated neurobiological features of PMDD are needed to promote timely diagnoses and inform development of prevention and treatment strategies.