Carnosine regulation of intracellular pH homeostasis promotes lysosome-dependent tumor immunoevasion.

Tumor cells and surrounding immune cells undergo metabolic reprogramming, leading to an acidic tumor microenvironment. However, it is unclear how tumor cells adapt to this acidic stress during tumor progression. Here we show that carnosine, a mobile buffering metabolite that accumulates under hypoxia in tumor cells, regulates intracellular pH homeostasis and drives lysosome-dependent tumor immune evasion. A previously unrecognized isoform of carnosine synthase, CARNS2, promotes carnosine synthesis under hypoxia. Carnosine maintains intracellular pH (pHi) homeostasis by functioning as a mobile proton carrier to accelerate cytosolic H+ mobility and release, which in turn controls lysosomal subcellular distribution, acidification and activity. Furthermore, by maintaining lysosomal activity, carnosine facilitates nuclear transcription factor X-box binding 1 (NFX1) degradation, triggering galectin-9 and T-cell-mediated immune escape and tumorigenesis. These findings indicate an unconventional mechanism for pHi regulation in cancer cells and demonstrate how lysosome contributes to immune evasion, thus providing a basis for development of combined therapeutic strategies against hepatocellular carcinoma that exploit disrupted pHi homeostasis with immune checkpoint blockade.

Human-Derived Induced GABAergic Progenitor Cells Improve Cognitive Function in Mice and Inhibit Astrocyte Activation with Anti-Inflammatory Exosomes.

OBJECTIVE: The role of gamma-aminobutyric acid-ergic (GABAergic) neuron impairment in Alzheimer's disease (AD), and if and how transplantation of healthy GABAergic neurons can improve AD, remain unknown. METHODS: Human-derived medial ganglionic eminence progenitors (hiMGEs) differentiated from programmed induced neural precursor cells (hiNPCs) were injected into the dentate gyrus region of the hippocampus (HIP). RESULTS: We showed that grafts migrate to the whole brain and form functional synaptic connections in amyloid precursor protein gene/ presenilin-1 (APP/PS1) chimeric mice. Following transplantation of hiMGEs, behavioral deficits and AD-related pathology were alleviated and defective neurons were repaired. Notably, exosomes secreted from hiMGEs, which are rich in anti-inflammatory miRNA, inhibited astrocyte activation in vitro and in vivo, and the mechanism was related to regulation of CD4+ Th1 cells mediated tumor necrosis factor (TNF) pathway. INTERPRETATION: Taken together, these findings support the hypothesis that hiMGEs transplantation is an alternative treatment for neuronal loss in AD and demonstrate that exosomes with anti-inflammatory activity derived from hiMGEs are important factors for graft survival. ANN NEUROL 2024.

Frontostriatothalamic effective connectivity and dopaminergic function in the psychosis continuum.

Dysfunction of fronto-striato-thalamic (FST) circuits is thought to contribute to dopaminergic dysfunction and symptom onset in psychosis, but it remains unclear whether this dysfunction is driven by aberrant bottom-up subcortical signalling or impaired top-down cortical regulation. We used spectral dynamic causal modelling of resting-state functional MRI to characterize the effective connectivity of dorsal and ventral FST circuits in a sample of 46 antipsychotic-naïve first-episode psychosis patients and 23 controls and an independent sample of 36 patients with established schizophrenia and 100 controls. We also investigated the association between FST effective connectivity and striatal 18F-DOPA uptake in an independent healthy cohort of 33 individuals who underwent concurrent functional MRI and PET. Using a posterior probability threshold of 0.95, we found that midbrain and thalamic connectivity were implicated as dysfunctional across both patient groups. Dysconnectivity in first-episode psychosis patients was mainly restricted to the subcortex, with positive symptom severity being associated with midbrain connectivity. Dysconnectivity between the cortex and subcortical systems was only apparent in established schizophrenia patients. In the healthy 18F-DOPA cohort, we found that striatal dopamine synthesis capacity was associated with the effective connectivity of nigrostriatal and striatothalamic pathways, implicating similar circuits to those associated with psychotic symptom severity in patients. Overall, our findings indicate that subcortical dysconnectivity is evident in the early stages of psychosis, that cortical dysfunction may emerge later in the illness, and that nigrostriatal and striatothalamic signalling are closely related to striatal dopamine synthesis capacity, which is a robust marker for psychosis.

Xanthatin induce DDP-resistance lung cancer cells apoptosis through regulation of GLUT1 mediated ROS accumulation.

Chemoresistance to cisplatin (DDP) therapy is a major obstacle that needs to be overcome in treating lung cancer patients. Xanthatin has been reported to exhibit an antitumor effect on various cancers, but the function of xanthatin in DDP-resistance lung cancer remains unclear. The study aimed to explore the effect and mechanisms of xanthatin on proliferation, apoptosis, and migration in DDP-resistance lung cancer cells. In the present study, xanthatin suppresses the expression of glucose transporter 1 (GLUT1), attenuates the pentose phosphate pathway (PPP), and causes ROS accumulation and apoptosis, thereby mitigating the antioxidative capacity in DDP-resistance cells. Previous studies have shown that GLUT1 is associated with resistance to platinum drugs. We found that GLUT1 was significantly increased in the DDP-resistant lung cancer cell line compared to the parental cell line, and xanthatin significantly downregulated GLUT1 expression in DDP-resistant lung cancer cells. Notably, overexpression of GLUT1 significantly reduced the production of ROS and increased cellular NADPH/NADP+ and GSH/GSSG ratios. Thus, these results suggest that xanthatin induces DDP-resistance lung cancer cells apoptosis through regulation of GLUT1-mediated ROS accumulation. These findings might provide a possible strategy for the clinical treatment of DDP-resistant lung cancer.

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.

Iron-siRNA Nanohybrids for Enhanced Chemodynamic Therapy via Ferritin Heavy Chain Downregulation.

Ferrous iron (Fe2+ ) has more potent hydroxyl radical (⋅OH)-generating ability than other Fenton-type metal ions, making Fe-based nanomaterials attractive for chemodynamic therapy (CDT). However, because Fe2+ can be converted by ferritin heavy chain (FHC) to nontoxic ferric form and then sequestered in ferritin, therapeutic outcomes of Fe-mediated CDT agents are still far from satisfactory. Here we report the synthesis of siRNA-embedded Fe0 nanoparticles (Fe0 -siRNA NPs) for self-reinforcing CDT via FHC downregulation. Upon internalization by cancer cells, pH-responsive Fe0 -siRNA NPs are degraded to release Fe2+ and FHC siRNA in acidic endo/lysosomes with the aid of oxygen (O2 ). The accompanied O2 depletion causes an intracellular pH decrease, which further promotes the degradation of Fe0 -siRNA NPs. In addition to initiating chemodynamic process, Fe2+ -catalyzed ⋅OH generation facilitates endo/lysosomal escape of siRNA by disrupting the membranes, enabling FHC downregulation-enhanced CDT.

Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation.

The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data-driven deep learning approach. A simulation framework was developed to generate motion-corrupted images from motion-free images using randomly generated motion profiles. An Inception-ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder-decoder network. The network was trained on simulated motion-corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real-world experimental motion-corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real-world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5-10% better for the proposed method. In conclusion, a novel, data-driven motion correction technique has been developed that can suppress motion artefacts from motion-corrupted MR images. The proposed technique is a standalone, post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.

Magnetic Resonance Iron Imaging in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) results in progressive impairment of upper and lower motor neurons. Increasing evidence from both in vivo and ex vivo studies suggest that iron accumulation in the motor cortex is a neuropathological hallmark in ALS. An in vivo neuroimaging marker of iron dysregulation in ALS would be useful in disease diagnosis and prognosis. Magnetic resonance imaging (MRI), with its unique capability to generate a variety of soft tissue contrasts, provides opportunities to image iron distribution in the human brain with millimeter to sub-millimeter anatomical resolution. Conventionally, MRI T1-weighted, T2-weighted, and T2*-weighted images have been used to investigate iron dysregulation in the brain in vivo. Susceptibility weighted imaging has enhanced contrast for para-magnetic materials that provides superior sensitivity to iron in vivo. Recently, the development of quantitative susceptibility mapping (QSM) has realized the possibility of using quantitative assessments of magnetic susceptibility measures in brain tissues as a surrogate measurement of in vivo brain iron. In this review, we provide an overview of MRI techniques that have been used to investigate iron dysregulation in ALS in vivo. The potential uses, strengths, and limitations of these techniques in clinical trials, disease diagnosis, and prognosis are presented and discussed. We recommend further longitudinal studies with appropriate cohort characterization to validate the efficacy of these techniques. We conclude that quantitative iron assessment using recent advances in MRI including QSM holds great potential to be a sensitive diagnostic and prognostic marker in ALS. The use of multimodal neuroimaging markers in combination with iron imaging may also offer improved sensitivity in ALS diagnosis and prognosis that could make a major contribution to clinical care and treatment trials. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

Dysregulation of non-coding RNAs mediates cisplatin resistance in hepatocellular carcinoma and therapeutic strategies.

Hepatocellular carcinoma (HCC) is the fourth major contributor to cancer-related deaths worldwide, and patients mostly have poor prognosis. Although several drugs have been approved for the treatment of HCC, cisplatin (CDDP) is still applied in treatment of HCC as a classical chemotherapeutic drug. Unfortunately, the emergence of CDDP resistance has caused HCC patients to exhibit poor drug response. How to mitigate or even reverse CDDP resistance is an urgent clinical issue to be solved. Because of critical roles in biological functional processes and disease developments, non-coding RNAs (ncRNAs) have been extensively studied in HCC in recent years. Importantly, ncRNAs have also been demonstrated to be involved in the development of HCC to CDDP resistance process. Therefore, this review highlighted the regulatory roles of ncRNAs in CDDP resistance of HCC, elucidated the multiple potential mechanisms by which HCC develops CDDP resistance, and attempted to propose multiple drug delivery systems to alleviate CDDP resistance. Recently, ncRNA-based therapy may be a feasible strategy to alleviate CDDP resistance in HCC. Meanwhile, nanoparticles can overcome the deficiencies in ncRNA-based therapy and make it possible to reverse tumor drug resistance. The combined use of these strategies provides clues for reversing CDDP resistance and overcoming the poor prognosis of HCC.

Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study.

Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain-glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of "connectivity," only across-subject "covariance." Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic process whereby individual brain connectivity cannot be inferred from group-level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity as well as multimodality biomarkers of neurological diseases.

Xanthatin inhibits human colon cancer cells progression via mTOR signaling mediated energy metabolism alteration.

Tumor cells exhibit higher glycolysis and rely on abnormal energy metabolism to produce ATP, which is essential for cell proliferation and migration. Abnormal energy metabolism inhibition is considered a promising tumor treatment strategy. Xanthatin is an active sesquiterpene lactone isolated from Xanthium strumarium L. This study evaluated the effect of xanthatin on the energy metabolism of human colon cancer cells. The results showed that xanthatin significantly inhibited the migration and invasion of human HT-29 and HCT-116 colon cancer cells. We found that xanthatin effectively reduced the production of ATP and promoted the accumulation of lactate. Xanthatin inhibited glycolysis which may be related to the reduction of glucose transporter 1 (Glut1) and monocarboxylate transporter 4 (MCT4) mRNA and protein levels. Concomitantly, xanthatin promoted complex II activity and oxidative phosphorylation (OXPHOS), resulting in mitochondrial damage and cell death in HT-29 cells. Furthermore, xanthatin inhibited the phosphorylation of mTOR, the phosphorylation of 4E-binding protein 1 (4E-BP1) and c-myc in HT-29 cells. Moreover, rapamycin, a mTOR inhibitor, could enhance the cytotoxicity effect in xanthatin treated HT-29 cells. Additionally, HT-29 cells transfected with si-mTOR aggravated xanthatin induced cell viability inhibition. Based on these results, we observed that the effect of xanthatin on energy metabolism may be related to its inhibition of the mTOR signaling pathway. Collectively, this study provides important insights into xanthatin's anticancer effect, which occurs by regulation of the energy metabolism of human colon cancer cells, and suggest that xanthatin has potential as a botanical drug against abnormal tumor energy metabolism.

Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET.

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.

Estimation of simultaneous BOLD and dynamic FDG metabolic brain activations using a multimodality concatenated ICA (mcICA) method.

Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity.

MR-PET head motion correction based on co-registration of multicontrast MR images.

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance-positron emission tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multicontrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR-guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.

Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging.

PURPOSE: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to improve the accuracy of estimated attenuation maps from MRI Dixon contrast images by training an augmented generative adversarial network (GANs) in a supervised manner. We augmented the GANs by perturbing the non-linear deformation field during image registration between MRI and the ground truth CT images. METHODS: We acquired the CT and the corresponding PET-MR images for a cohort of 28 prostate cancer patients. Data from 18 patients (2160 slices and later augmented to 270,000 slices) was used for training the GANs and others for validation. We calculated the error in bone and soft tissue regions for the AC µ-maps and the reconstructed PET images. RESULTS: For quantitative analysis, we use the average relative absolute errors and validate the proposed technique on 10 patients. The DL-based MR methods generated the pseudo-CT AC µ-maps with an accuracy of 4.5% more than standard MR-based techniques. Particularly, the proposed method demonstrates improved accuracy in the pelvic regions without affecting the uptake values. The lowest error of the AC µ-map in the pelvic region was 1.9% for µ-mapGAN + aug compared with 6.4% for µ-mapdixon, 5.9% for µ-mapdixon + bone, 2.1% for µ-mapU-Net and 2.0% for µ-mapU-Net + aug. For the reconstructed PET images, the lowest error was 2.2% for PETGAN + aug compared with 10.3% for PETdixon, 8.7% for PETdixon + bone, 2.6% for PETU-Net and 2.4% for PETU-Net + aug.. CONCLUSION: The proposed technique to augment the training datasets for training of the GAN results in improved accuracy of the estimated µ-map and consequently the PET quantification compared to the state of the art.

Screening for differentially expressed circRNAs in ischemic stroke by RNA sequencing.

BACKGROUND: Ischemic stroke is a disease with high rate of death and disability worldwide. CircRNAs, as a novel type of non-coding RNAs, lacking 5' caps and 3' poly-A tails, has been associated with ischemic stroke. This study aimed to investigate key circRNAs related to ischemic stroke. METHODS: RNA sequencing was performed obtain the circRNA expression profiles from peripheral whole blood of three ischemic stroke patients and three healthy individuals. Through bioinformatic analysis, differentially expressed circRNAs (DEcircRNAs) were identified, and GO and pathway analyses for the host genes of DEcircRNAs were conducted. The expression levels of selected circRNAs were analyzed with qRT-PCR. To further explore the functions of key circRNAs, a DEcircRNA-miRNA interaction network was constructed. RESULTS: A total of 736 DEcircRNAs were detected in ischemic stroke. Functional annotation of host genes of DEcircRNAs revealed several significantly enriched pathways, including Fc epsilon RI signaling pathway, B cell receptor signaling pathway, and T cell receptor signaling pathway. The qRT-PCR results were largely in keeping with our RNA-seq data. The ROC curve analyses indicated that hsa_circ_0000745, hsa_circ_0001459, hsa_circ_0003694 and hsa_circ_0007706 with relatively high diagnostic value. A circRNA-miRNA network, including 1544 circRNA-miRNA pairs, 456 circRNAs and 4 miRNAs, was obtained. CONCLUSIONS: The results of our study may help to elucidate the specific mechanism underlying ischemic stroke.

Analysis of continuous infusion functional PET (fPET) in the human brain.

Functional positron emission tomography (fPET) is a neuroimaging method involving continuous infusion of 18-F-fluorodeoxyglucose (FDG) radiotracer during the course of a PET examination. Compared with the conventional bolus administration of FDG in a static PET scan, which provides an average glucose uptake into the brain over an extended period of up to 30 â€‹min, fPET offers a significantly higher temporal resolution to study the dynamics of glucose uptake. Several earlier studies have applied fPET to investigate brain FDG uptake and study its relationship with functional magnetic resonance imaging (fMRI). However, due to the unique characteristics of fPET signals, modelling of the fPET signal is a complex task and poses challenges for accurate interpretation of the results from fPET experiments. This study applied independent component analysis (ICA) to analyse resting state fPET data, and to compare the performance of ICA and the general linear model (GLM) for estimation of brain activation in response to tasks. The fPET signal characteristics were compared using GLM and ICA methods to model fPET data from a visual activation experiment. Our aim was to evaluate GLM and ICA methods for analysing task fPET datasets, and to apply ICA methods to the analysis of resting state fPET datasets. Using both simulation and in-vivo experimental datasets, we show that both ICA and GLM methods can successfully identify task related brain activation. We report fPET metabolic resting state brain networks revealed by application of the fPET ICA method to a cohort of 28 healthy subjects. Functional PET provides a unique method to map dynamic changes of glucose uptake in the resting human brain and in response to extrinsic stimulation.

Simultaneous task-based BOLD-fMRI and [18-F] FDG functional PET for measurement of neuronal metabolism in the human visual cortex.

Studies of task-evoked brain activity are the cornerstone of cognitive neuroscience, and unravel the spatial and temporal brain dynamics of cognition in health and disease. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI) is one of the most common methods of studying brain function in humans. BOLD-fMRI indirectly infers neuronal activity from regional changes in blood oxygenation and is not a quantitative metric of brain function. Regional variation in glucose metabolism, measured using [18-F] fluorodeoxyglucose positron emission tomography (FDG-PET), provides a more direct and interpretable measure of neuronal activity. However, while the temporal resolution of BOLD-fMRI is in the order of seconds, standard FDG-PET protocols provide a static snapshot of glucose metabolism. Here, we develop a novel experimental design for measurement of task-evoked changes in regional blood oxygenation and glucose metabolism with high temporal resolution. Over a 90-min simultaneous BOLD-fMRI/FDG-PET scan, [18F] FDG was constantly infused to 10 healthy volunteers, who viewed a flickering checkerboard presented in a hierarchical block design. Dynamic task-related changes in blood oxygenation and glucose metabolism were examined with temporal resolution of 2.5sec and 1-min, respectively. Task-related, temporally coherent brain networks of haemodynamic and metabolic connectivity were jointly coupled in the visual cortex, as expected. Results demonstrate that the hierarchical block design, together with the infusion FDG-PET technique, enabled both modalities to track task-related neural responses with high temporal resolution. The simultaneous MR-PET approach has the potential to provide unique insights into the dynamic haemodynamic and metabolic interactions that underlie cognition in health and disease.