FDG PET in the Evaluation of Parkinson's Disease

Parkinson's disease cerebrovascular reactivity pattern: A feasibility study

A mounting body of research points to cerebrovascular dysfunction as a fundamental element in the pathophysiology of Parkinson's disease (PD). In the current feasibility study, blood-oxygen-level-dependent (BOLD) MRI was used to measure cerebrovascular reactivity (CVR) in response to hypercapnia in 26 PD patients and 16 healthy controls (HC), and aimed to find a multivariate pattern specific to PD. Whole-brain maps of CVR amplitude (i.e., magnitude of response to CO2) and latency (i.e., time to reach maximum amplitude) were computed, which were further analyzed using scaled sub-profile model principal component analysis (SSM-PCA) with leave-one-out cross-validation. A meaningful pattern based on CVR latency was identified, which was named the PD CVR pattern (PD-CVRP). This pattern was characterized by relatively increased latency in basal ganglia, sensorimotor cortex, supplementary motor area, thalamus and visual cortex, as well as decreased latency in the cerebral white matter, relative to HC. There were no significant associations with clinical measures, though sample size may have limited our ability to detect significant associations. In summary, the PD-CVRP highlights the importance of cerebrovascular dysfunction in PD, and may be a potential biomarker for future clinical research and practice.

Pragmatic Approach on Neuroimaging Techniques for the Differential Diagnosis of Parkinsonisms

Background

Rapid advances in neuroimaging technologies in the exploration of the living human brain also apply to movement disorders. However, the accurate diagnosis of Parkinson's disease (PD) and atypical parkinsonian disorders (APDs) still remains a challenge in daily practice.

Methods

We review the literature and our own experience as the Movement Disorder Society-Neuroimaging Study Group in Movement Disorders with the aim of providing a practical approach to the use of imaging technologies in the clinical setting.

Results

The enormous amount of articles published so far and our increasing recognition of imaging technologies contrast with a lack of imaging protocols and updated algorithms for differential diagnosis. The distinctive pathological involvement in different brain structures and the correlation with imaging findings obtained with magnetic resonance, positron emission tomography, or single-photon emission computed tomography illustrate what qualitative and quantitative measures may be useful in the clinical setting.

Conclusion

We delineate a pragmatic approach to discuss imaging technologies, updated imaging algorithms, and their implications for differential diagnoses in PD and APDs.

Cognitive impairment in Parkinson's disease: A clinical and pathophysiological overview

Cognitive dysfunction in Parkinson's disease (PD) has received increasing attention, and, together with other non-motor symptoms, exert a significant functional impact in the daily lives of patients. This article aims to compile and briefly summarize selected published data about clinical features, cognitive evaluation, biomarkers, and pathophysiology of PD-related dementia (PDD). The literature search included articles indexed in the MEDLINE/PubMed database, published in English, over the last two decades. Despite significant progress on clinical criteria and cohort studies for PD-mild cognitive impairment (PD-MCI) and PDD, there are still knowledge gaps about its exact molecular and pathological basis. Here we overview the scientific literature on the role of functional circuits, neurotransmitter systems (monoaminergic and cholinergic), basal forebrain, and brainstem nuclei dysfunction in PD-MCI. Correlations between neuroimaging and cerebrospinal fluid (CSF) biomarkers, clinical outcomes, and pathological results are described to aid in uncovering the neurodegeneration pattern in PD-MCI and PDD.

MRI biomarkers of motor and non-motor symptoms in Parkinson's disease

Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

The Metabolic Activity of Caudate and Prefrontal Cortex Negatively Correlates with the Severity of Idiopathic Parkinson's Disease

Positron emission tomography (PET) scan with tracer [¹⁸F]-fluorodeoxy-glucose (¹⁸F-FDG) is widely used to measure the glucose metabolism in neurodegenerative disease such as Idiopathic Parkinson’s disease (IPD). Previous studies using ¹⁸F-FDG PET mainly focused on the motor or non-motor symptoms but not the severity of IPD. In this study, we aimed to determine the metabolic patterns of ¹⁸F-FDG in different stages of IPD defined by Hoehn and Yahr rating scale (H-Y rating scale) and to identify regions in the brain that play critical roles in disease progression. Fifty IPD patients were included in this study. They were 29 men and 21 women (mean±SD, age 57.7±11.1 years, disease duration 4.0±3.8 years, H-Y 2.2±1.1). Twenty healthy individuals were included as normal controls. Following ¹⁸F-FDG PET scan, image analysis was performed using Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST). The metabolic feature of IPD and regions-of-interests (ROIs) were determined. Correlation analysis between ROIs and H-Y stage was performed. SPM analysis demonstrated a significant hypometabolic activity in bilateral putamen, caudate and anterior cingulate as well as left parietal lobe, prefrontal cortex in IPD patients. In contrast, hypermetabolism was observed in the cerebellum and vermis. There was a negative correlation (p=0.007, r=-0.412) between H-Y stage and caudate metabolic activity. Moreover, the prefrontal area also showed a negative correlation with H-Y (P=0.033, r=-0.334). Thus, the uptake of FDG in caudate and prefrontal cortex can potentially be used as a surrogate marker to evaluate the severity of IPD.

Data-driven, voxel-based analysis of brain PET images: Application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration

Spatial patterns of radiotracer binding in positron emission tomography (PET) images may convey information related to the disease topology. However, this information is not captured by the standard PET image analysis that quantifies the mean radiotracer uptake within a region of interest (ROI). On the other hand, spatial analyses that use more advanced radiomic features may be difficult to interpret. Here we propose an alternative data-driven, voxel-based approach to spatial pattern analysis in brain PET, which can be easily interpreted. We apply principal component analysis (PCA) to identify voxel covariance patterns, and optimally combine several patterns using the least absolute shrinkage and selection operator (LASSO). The resulting models predict clinical disease metrics from raw voxel values, allowing for inclusion of clinical covariates. The analysis is performed on high-resolution PET images from healthy controls and subjects affected by Parkinson’s disease (PD), acquired with a pre-synaptic and a post-synaptic dopaminergic PET tracer. We demonstrate that PCA identifies robust and tracer-specific binding patterns in sub-cortical brain structures; the patterns evolve as a function of disease progression. Principal component LASSO (PC-LASSO) models of clinical disease metrics achieve higher predictive accuracy compared to the mean tracer binding ratio (BR) alone: the cross-validated test mean squared error of adjusted disease duration (motor impairment score) was 16.3 ± 0.17 years² (9.7 ± 0.15) with mean BR, versus 14.4 ± 0.18 years² (8.9 ± 0.16) with PC-LASSO. We interpret the best-performing PC-LASSO models in the spatial sense and discuss them with reference to the PD pathology and somatotopic organization of the striatum. PC-LASSO is thus shown to be a useful method to analyze clinically-relevant tracer binding patterns, and to construct interpretable, imaging-based predictive models of clinical metrics.

Recent Advances in Biomarkers for Parkinson's Disease

Parkinson's disease (PD) is one of the common progressive neurodegenerative disorders with several motor and non-motor symptoms. Most of the motor symptoms may appear at a late stage where most of the dopaminergic neurons have been already damaged. In order to provide better clinical intervention and treatment at the onset of disease, it is imperative to find accurate biomarkers for early diagnosis, including prodromal diagnosis and preclinical diagnosis. At the same time, these reliable biomarkers can also be utilized to monitor the progress of the disease. In this review article, we will discuss recent advances in the development of PD biomarkers from different aspects, including clinical, biochemical, neuroimaging and genetic aspects. Although various biomarkers for PD have been developed so far, their specificity and sensitivity are not ideal when applied individually. So, the combination of multimodal biomarkers will greatly improve the diagnostic accuracy and facilitate the implementation of personalized medicine.

99mTc-TRODAT-1 SPECT/CT imaging as a complementary biomarker in the diagnosis of parkinsonian syndromes

INTRODUCTION

Parkinson's disease (PD) and Parkinson plus syndromes (PPS) are neurodegenerative movement disorders caused by loss of dopamine in the basal ganglia. The diagnosis of both PD and PPS is complex as it is made solely on the basis of clinical features, with no established imaging modality to aid in the diagnosis. Technetium-99m-labeled tropane derivative (Tc-TRODAT-1) binds to the dopamine transporters present in the presynaptic membrane of the dopaminergic nerve terminal. The aim of this prospective study was to investigate the potential usefulness of Tc-TRODAT-1 imaging in the diagnosis of PD and PPS.

PATIENTS AND METHODS

Fifty-eight patients with a clinical diagnosis of idiopathic PD or PPS were recruited. The severity of the disease was assessed using the Hoehn and Yahr scale. Patients in stage I and II were considered as cases of Early PD. Twenty-five apparently healthy volunteers served as controls. Brain single-photon emission computed tomography/computed tomography in all the participants was performed 3-4 h after an injection of Tc-TRODAT-1. Specific uptake ratios (SURs) of striatum were calculated for both the left and right striatum, and the values were compared between PD, PPS, and healthy volunteers.

RESULTS

A significant lower uptake of tracer activity was found in either of the striatum in PD and PPS cases compared with the control group, which showed a symmetrical comma-shaped striatal uptake. This was also reflected in the SUR values, which were significantly higher in the control group in comparison with the PD and PPS patients (P<0.001). A significant difference was also found in the SUR values between the cases of early PD and control group (P<0.001).No significant difference was noted among the SUR values in different Hoehn and Yahr stages.

CONCLUSION

For clinical practice, both the visual analysis and the quantitative parameters of Tc-TRODAT-1 single-photon emission computed tomography/computed tomography showed usefulness in distinguishing cases of PD and PPS from the healthy individuals.

The effects of image reconstruction algorithms on topographic characteristics, diagnostic performance and clinical correlation of metabolic brain networks in Parkinson's disease

PURPOSE

The purpose of this study was to evaluate the effects of different image reconstruction algorithms on topographic characteristics and diagnostic performance of the Parkinson's disease related pattern (PDRP).

METHODS

FDG-PET brain scans of 20 Parkinson's disease (PD) patients and 20 normal controls (NC) were reconstructed with six different algorithms in order to derive six versions of PDRP. Additional scans of 20 PD, 25 atypical parkinsonism (AP) patients and 20 NC subjects were used for validation. PDRP versions were compared by assessing differences in topographies, individual subject scores and correlations with patient's clinical ratings. Discrimination of PD from NC and AP subjects was evaluated across cohorts.

RESULTS

The region weights of the six PDRPs highly correlated (R ≥ 0.991; p < 0.0001). All PDRPs' expressions were significantly elevated in PD relative to NC and AP subjects (p < 0.0001) and correlated with clinical ratings (R ≥ 0.47; p < 0.05). Subject scores of the six PDRPs highly correlated within each of individual healthy and parkinsonian groups (R ≥ 0.972, p < 0.0001) and were consistent across the algorithms when using the same reconstruction methods in PDRP derivation and validation. However, when derivation and validation reconstruction algorithms differed, subject scores were notably lower compared to the reference PDRP, in all subject groups.

CONCLUSION

PDRP proves to be highly reproducible across FDG-PET image reconstruction algorithms in topography, ability to differentiate PD from NC and AP subjects and clinical correlation. When calculating PDRP scores in scans that have different reconstruction algorithms and imaging systems from those used for PDRP derivation, a calibration with NC subjects is advisable.

Clinical utility of FDG PET in Parkinson's disease and atypical parkinsonism associated with dementia

Purpose

There are no comprehensive guidelines for the use of FDG PET in the following three clinical scenarios: (1) diagnostic work-up of patients with idiopathic Parkinson’s disease (PD) at risk of future cognitive decline, (2) discriminating idiopathic PD from progressive supranuclear palsy, and (3) identifying the underlying neuropathology in corticobasal syndrome.

Methods

We therefore performed three literature searches and evaluated the selected studies for quality of design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were the sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiving operating characteristic curve, and positive/negative likelihood ratio of FDG PET in detecting the target condition. Using the Delphi method, a panel of seven experts voted for or against the use of FDG PET based on published evidence and expert opinion.

Results

Of 91 studies selected from the three literature searches, only four included an adequate quantitative assessment of the performance of FDG PET. The majority of studies lacked robust methodology due to lack of critical outcomes, inadequate gold standard and no head-to-head comparison with an appropriate reference standard. The panel recommended the use of FDG PET for all three clinical scenarios based on nonquantitative evidence of clinical utility.

Conclusion

Despite widespread use of FDG PET in clinical practice and extensive research, there is still very limited good quality evidence for the use of FDG PET. However, in the opinion of the majority of the panellists, FDG PET is a clinically useful imaging biomarker for idiopathic PD and atypical parkinsonism associated with dementia.

Diagnosing Parkinson Disease

PURPOSE OF REVIEW

While establishing the diagnosis of Parkinson disease (PD) can be straightforward, it can be challenging in some patients, even for the experienced neurologist. The misdiagnosis rate ranges from 10% to 20% or greater depending on clinician experience.

RECENT FINDINGS

Despite promise in the search for a biomarker that can establish the presence of PD and act as a marker of its progression, the diagnosis of PD continues to be based on clinical examination. Core criteria, exclusion criteria, and supportive criteria have been developed to aid the clinician in establishing the diagnosis. Nonmotor symptoms of PD are usually present at the time of diagnosis, may precede motor symptoms, and should be specifically sought during evaluation. Ancillary testing can be appropriate, but its indications and utility must be clearly understood.

SUMMARY

The diagnosis of PD requires the recognition of the core features of PD and the differentiation of its clinical presentation from other entities with similar and potentially overlapping symptoms. A careful history and examination guided by clinical diagnostic criteria will usually establish the diagnosis of PD or uncover red flags for the possibilities of other diagnoses. Appropriate selection and interpretation of ancillary testing is critical to avoid misdiagnosis and unnecessary tests.

Imaging biomarkers in Parkinson's disease and Parkinsonian syndromes: current and emerging concepts

Two centuries ago in 1817, James Parkinson provided the first medical description of Parkinson’s disease, later refined by Jean-Martin Charcot in the mid-to-late 19th century to include the atypical parkinsonian variants (also termed, Parkinson-plus syndromes). Today, Parkinson’s disease represents the second most common neurodegenerative disorder with an estimated global prevalence of over 10 million. Conversely, atypical parkinsonian syndromes encompass a group of relatively heterogeneous disorders that may share some clinical features with Parkinson’s disease, but are uncommon distinct clinicopathological diseases. Decades of scientific advancements have vastly improved our understanding of these disorders, including improvements in in vivo imaging for biomarker identification. Multimodal imaging for the visualization of structural and functional brain changes is especially important, as it allows a ‘window’ into the underlying pathophysiological abnormalities. In this article, we first present an overview of the cardinal clinical and neuropathological features of, 1) synucleinopathies: Parkinson’s disease and other Lewy body spectrum disorders, as well as multiple system atrophy, and 2) tauopathies: progressive supranuclear palsy, and corticobasal degeneration. A comprehensive presentation of well-established and emerging imaging biomarkers for each disorder are then discussed. Biomarkers for the following imaging modalities are reviewed: 1) structural magnetic resonance imaging (MRI) using T1, T2, and susceptibility-weighted sequences for volumetric and voxel-based morphometric analyses, as well as MRI derived visual signatures, 2) diffusion tensor MRI for the assessment of white matter tract injury and microstructural integrity, 3) proton magnetic resonance spectroscopy for quantifying proton-containing brain metabolites, 4) single photon emission computed tomography for the evaluation of nigrostriatal integrity (as assessed by presynaptic dopamine transporters and postsynaptic dopamine D2 receptors), and cerebral perfusion, 5) positron emission tomography for gauging nigrostriatal functions, glucose metabolism, amyloid and tau molecular imaging, as well as neuroinflammation, 6) myocardial scintigraphy for dysautonomia, and 7) transcranial sonography for measuring substantia nigra and lentiform nucleus echogenicity. Imaging biomarkers, using the ‘multimodal approach’, may aid in making early, accurate and objective diagnostic decisions, highlight neuroanatomical and pathophysiological mechanisms, as well as assist in evaluating disease progression and therapeutic responses to drugs in clinical trials.

Role of ¹⁸F-FDG PET imaging in paediatric primary dystonia and dystonia arising from neurodegeneration with brain iron accumulation

PURPOSE

No current neuroimaging modality offers mechanistic or prognostic information to guide management in paediatric dystonia. We assessed F-fluorodeoxyglucose (¹⁸F-FDG) PET/computed tomography (CT) brain imaging in childhood primary dystonia (PDS) and neurodegeneration with brain iron accumulation (NBIA) to determine whether it would identify altered metabolism and hence constitute a potentially useful 'biomarker' indicating functional disturbances associated with dystonia and severity of the disease.

MATERIALS AND METHODS

A total of 27 children (15 PDS and 12 NBIA) underwent brain ¹⁸F-FDG PET/CT imaging under anaesthesia during acquisition. The images were assessed visually and the two groups were compared quantitatively with statistical parametric mapping. PET/CT images were spatially transformed to Montreal Neurological Institute standard space. Voxelwise ¹⁸F-FDG uptake was normalized to whole-brain uptake. Data of both groups were correlated separately with duration and severity of dystonia as assessed using the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS).

RESULTS

Visual inspection did not identify any abnormalities in ¹⁸F-FDG uptake within the cerebral cortex, basal ganglia, or thalami in either group. Quantitative analysis identified higher uptake in the posterior cingulate and bilateral posterior putamina but decreased uptake in the occipital cortex and cerebellum in NBIA compared with PDS. The NBIA group had more severe dystonia scores compared with the PDS group. BFMDRS was negatively correlated with age but not with duration of dystonia.

CONCLUSION

Compared with PDS, NBIA is dominated by relative overactivity in the putamen and by cerebellar underactivity, patterns that may reflect the increased severity of dystonia in NBIA cases. Hence, there is a potential role for ¹⁸F-FDG PET/CT imaging in paediatric dystonia, particularly in the NBIA group.

Dopamine: PET Imaging and Parkinson Disease

This article discusses the current use of PET imaging in the evaluation of dopamine function in Parkinson disease (PD). The article reviews the major radioligands targeting dopaminergic systems in patients with parkinsonian disorders. The primary objective is to show the novel clinical applications of molecular imaging in the diagnosis and assessment of motor and nonmotor symptoms in PD.

Clinical applications of neuroimaging in patients with Alzheimer's disease: a review from the Fourth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia 2012

In May 2012, the Fourth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia brought together in Montreal experts from around Canada to update Canadian recommendations for the diagnosis and management of patients with neurodegenerative conditions associated with deterioration of cognition. Multiple topics were discussed. The present paper is a highly condensed version of those recommendations that were produced to support discussions in the field of neuroimaging for clinical diagnosis of those conditions.

The history of cerebral PET scanning: from physiology to cutting-edge technology

OBJECTIVE

To review the discoveries underpinning the introduction of cerebral PET scanning and highlight its modern applications.

BACKGROUND

Important discoveries in neurophysiology, brain metabolism, and radiotracer development in the post-World War II period provided the necessary infrastructure for the first cerebral PET scan.

METHODS

A complete review of the literature was undertaken to search for primary and secondary sources on the history of PET imaging. Searches were performed in PubMed, Google Scholar, and select individual journal Web sites. Written autobiographies were obtained through the Society for Neuroscience Web site at www.sfn.org. A reference book on the history of radiology, Naked to the Bone, was reviewed to corroborate facts and to locate references. The references listed in all the articles and books obtained were reviewed.

RESULTS

The neurophysiologic sciences required to build cerebral PET imaging date back to 1878. The last 60 years have produced an evolution of technological advancements in brain metabolism and radiotracer development. These advancements facilitated the development of modern cerebral PET imaging. Several key scientists were involved in critical discoveries and among them were Angelo Mosso, Charles Roy, Charles Sherrington, John Fulton, Seymour Kety, Louis Sokoloff, David E. Kuhl, Gordon L. Brownell, Michael Ter-Pogossian, Michael Phelps, and Edward Hoffman.

CONCLUSIONS

Neurophysiology, metabolism, and radiotracer development in the postwar era synergized the development of the technology necessary for cerebral PET scanning. Continued use of PET in clinical trials and current developments in PET-CT/MRI hybrids has led to advancement in diagnosis, management, and treatment of neurologic disorders.

Biomarkers for the diagnosis and management of Parkinson's disease

INTRODUCTION

Parkinson's disease (PD) is the most common neurodegenerative disease leading to movement disorders, and is characterized neuropathologically by the progressive loss of dopaminergic neurons, intracellular α-synuclein deposition and the formation of Lewy bodies. The difficulty of making a definitive diagnosis of PD itself, as opposed to other neurodegenerative diseases associated with parkinsonism, is a central issue in clinical PD research. However, recent advances in diagnostic methods, encompassing imaging techniques, genetic testing and measurement of biological markers may permit earlier diagnosis, and thus potentially improved management of PD.

AREAS COVERED

In addition to clinical symptoms and imaging techniques, a number of genetic and biological markers obtained from body fluids such as cerebrospinal fluids may hold promise for the early detection of PD. It is often difficult to make an accurate diagnosis and to distinguish PD from other diseases with features of parkinsonism, particularly during the early stages of the disease. In this regard, biomarkers which are specific for PD, in combination with observation of clinical symptoms, may facilitate the early diagnosis and improved management of PD.

EXPERT OPINION

Good biomarkers for PD could be helpful for early diagnosis, management and tracking of disease progression. Furthermore, combined analysis using several kinds of biomarkers may allow the detection of preclinical PD, which in turn may facilitate a prevention of disease onset with the use of disease-modifying drugs.

Magnetic resonance imaging of resting OEF and CMRO₂ using a generalized calibration model for hypercapnia and hyperoxia

We present a method allowing determination of resting cerebral oxygen metabolism (CMRO₂) from MRI and end-tidal O₂ measurements acquired during a pair of respiratory manipulations producing different combinations of hypercapnia and hyperoxia. The approach is based on a recently introduced generalization of calibrated MRI signal models that is valid for arbitrary combinations of blood flow and oxygenation change. Application of this model to MRI and respiratory data during a predominantly hyperoxic gas manipulation yields a specific functional relationship between the resting BOLD signal M and the resting oxygen extraction fraction OEF₀. Repeating the procedure using a second, primarily hypercapnic, manipulation provides a different functional form of M vs. OEF₀. These two equations can be readily solved for the two unknowns M and OEF₀. The procedure also yields the resting arterial O₂ content, which when multiplied by resting cerebral blood flow provides the total oxygen delivery in absolute physical units. The resultant map of oxygen delivery can be multiplied by the map of OEF₀ to obtain a map of the resting cerebral metabolic rate of oxygen consumption (CMRO₂) in absolute physical units. Application of this procedure in a group of seven human subjects provided average values of 0.35 ± 0.04 and 6.0 ± 0.7% for OEF₀ and M, respectively in gray-matter (M valid for 30 ms echo-time at 3T). Multiplying OEF₀ estimates by the individual values of resting gray-matter CBF (mean 52 ± 5 ml/100 g/min) and the measured arterial O₂ content gave a group average resting CMRO₂ value of 145 ± 30 μmol/100 g/min. The method also allowed the generation of maps depicting resting OEF, BOLD signal, and CMRO₂.