Regional metabolic changes in Parkinsonian patients with normal dopaminergic imaging

Unexpected (123I)FP-CIT SPECT findings: SWIDD, SWEDD and all DAT

Although the diagnosis of Parkinson's disease (PD) is essentially clinical, the implementation of imaging techniques can improve diagnostic accuracy. While some techniques (e.g. magnetic resonance imaging-MRI, computerized tomography-CT) are used to exclude secondary syndromes, presynaptic dopaminergic imaging including imaging of dopamine transporter (DAT)-can help the Neurologist in the differential diagnosis between neurodegenerative parkinsonian syndromes and parkinsonism without dopamine deficiency. DAT imaging can be useful in cases in which the clinical picture is not univocal, as in case of overlapping clinical features in patients with early disease, atypical syndromes or unsatisfying response to therapy. Currently, (¹²³I)FP-CIT ([123I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane) (trade name DaTSCAN) is the only agent approved by international regulatory agencies for this purpose. With the increasing use of this technique, some unexpected findings have been reported, including patients clinically diagnosed with PD with a normal SPECT scan [e.g. Scans Without Evidence of Dopaminergic Deficit (SWEDD)]; PD patients with a greater dopaminergic deficit in the striatum ipsilateral to the clinically more affected side [e.g. Scans With Ipsilateral Dopaminergic Deficit (SWIDD)]; as well as some artifacts. Moreover, the neurologist must remember that structural lesions and administration of some drugs might alter the result of DAT imaging. Unexpected findings, artifacts, and misinterpretation of imaging findings can lead to an erroneous diagnosis and inappropriate therapy, neglect of other medical conditions that might explain the clinical picture, and undermine the selection phase in clinical trials. The aim of the present review is to bring clarity on these controversial (and sometimes erroneous) results, in order to inform of these possibilities the clinicians requesting a DaTSCAN in clinical practice.

FDG PET Parkinson's disease-related pattern as a biomarker for clinical trials in early stage disease

Background

The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects.

Objectives

We sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD.

Methods

Two machine learning approaches (Scaled Subprofile Model (SSM) and NPAIRS with Canonical Variates Analysis) were performed on FDG-PET scans from 17 healthy controls (HC) and 23 PD patients. The approaches were compared regarding discrimination of HC from PD and relationship to motor symptoms.

Results

Both classifiers discriminated HC from PD (p < 0.01, p < 0.03), and classifier scores for age- and gender- matched HC and PD correlated with Hoehn & Yahr stage (R² = 0.24, p < 0.015) and UPDRS (R² = 0.23, p < 0.018). Metabolic patterns were highly similar, with hypometabolism in parieto-occipital and prefrontal regions and hypermetabolism in cerebellum, pons, thalamus, paracentral gyrus, and lentiform nucleus relative to whole brain, consistent with the PDRP. An additional classifier was developed using only PD subjects, resulting in scores that correlated with UPDRS (R² = 0.25, p < 0.02) and Hoehn & Yahr stage (R² = 0.16, p < 0.06).

Conclusions

Two independent analyses performed in a cohort of mild PD patients replicated key features of the PDRP, confirming that FDG-PET and multivariate classification can provide an objective, sensitive biomarker of disease stage with the potential to detect treatment effects on PD progression.

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The Parkinson's disease-related pattern (PDRP) of glucose metabolic effects is demonstrated in an independent cohort of early stage PD patients.

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The PDRP pattern of metabolic changes is robust to variations in image processing and choice of classification model.

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Age-related metabolic changes show partial overlap with the PDRP, suggesting that age-adjustment is an important consideration.

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The PDRP correlates with motor function as defined by Hoehn & Yahr stage and UPDRS score.

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An additional data driven metabolic classifier highlights pattern aspects associated with early stage motor decline.

Overview of neuroradiology

Neuroradiology with computed tomography (CT) and magnetic resonance imaging (MRI) is essential for the initial evaluation of patients with a clinical suspicion of brain and spine disorders. Morphologic imaging is required to obtain a probable diagnosis to support the treatment decisions in pre- and perinatal disorders, vascular diseases, traumatic injuries, metabolic disorders, epilepsy, infection/inflammation, neurodegenerative disorders, degenerative spinal disease, and tumors of the central nervous system. Different postprocessing tools are increasingly used for three-dimensional visualization and quantification of lesions. Additional information is provided by angiographic methods and physiologic CT and MRI techniques, such as diffusion MRI, perfusion CT/MRI, MR spectroscopy, functional MRI, tractography, and nuclear medicine imaging methods. Positron emission tomography (PET) is now integrated with CT (PET/CT), and PET/MR scanners have recently also been introduced. These hybrid techniques facilitate the co-registration of lesions with different modalities, and give new possibilites for functional imaging. Repeated imaging is increasingly performed for treatment monitoring. The improved imaging techniques together with the neuropathologic diagnosis after biopsy or surgery allow more personalized treatment of the patient. Neuroradiology also includes endovascular treatment of aneurysms and arteriovenous malformations as well as thrombectomy in acute stroke. This catheter-based treatment has replaced invasive neurosurgery in many cases.

Suite PET/CT neuroimaging for the diagnosis of Parkinson's disease: statistical parametric mapping analysis

OBJECTIVES

The aim of this study was to investigate the topographical distribution of dopamine transporter (DAT), dopamine D2 receptor, and glucose metabolism in Parkinson's disease (PD) using PET/computed tomography (CT) scanning and statistical parametric mapping (SPM) analysis.

PARTICIPANTS AND METHODS

Seventy-four patients (58 PD patients and 16 normal controls) underwent DAT, D2 receptor, and glucose brain PET/CT scans using C-methyl-N-2-β-carbomethoxy-3-β-(4-fluorophenyl) tropane (C-β-CFT), C-raclopride (C-RAC), and fluorine-18-fluorodeoxyglucose (F-FDG) radiotracers for the respective scans. All three PET/CT procedures were performed in each participant. The uptake patterns were analyzed using SPM software.

RESULTS

Striatal DAT binding was lower in PD patients than in controls, whereas D2 receptor binding did not differ between PD patients and controls. D2 receptor binding was increased in the putamen in only the 12 drug-naive patients. Glucose uptake was also slightly lower in the cingulate gyrus of PD patients than in the controls.

CONCLUSION

Suite PET/CT scans using the ligands C-β-CFT, C-RAC, and F-FDG PET/CT are valuable for diagnosing PD. SPM-based analysis of static PET/CT scan data is potentially of great clinical use.

What do patients with scans without evidence of dopaminergic deficit (SWEDD) have? New evidence and continuing controversies

The term SWEDD (scans without evidence for dopaminergic deficit) refers to the absence, rather than the presence, of an imaging abnormality in patients clinically presumed to have Parkinson's disease (PD). However, such a term has since been widely used in the medical literature, even as a diagnostic label. While many authors have suggested that different disorders of PD lookalikes may account for a proportion of SWEDD cases, others have claimed that some of them may have a benign subtype of PD. Thus, there has been ensuing controversy and confusion and the use of this term continues without clarity of what it represents. We have systematically reviewed all the studies involving patients with SWEDD with the aim of shedding light on what these patients actually have. It becomes clear from this systematic review that while most 'SWEDD' cases are due to a clinical misdiagnosis of PD, there exists a small proportion of patients with SWEDD who may have PD on the basis of a positive levodopa response, clinical progression, imaging and/or genetic evidence. The latter challenge the seemingly incontrovertible relationship between dopaminergic tracer binding and the diagnosis of nigrostriatal parkinsonism, particularly PD. Patients with SWEDD are unlikely to reflect a single clinical entity and we suggest that the term SWEDD should be abandoned.

Increased connectivity between sensorimotor and attentional areas in Parkinson's disease

INTRODUCTION

Our study is using Independent Component Analysis (ICA) to evaluate functional connectivity changes in Parkinson's disease (PD) in an unbiased manner.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) data was collected for 27 PD patients and 16 healthy subjects. Differences for intra- and inter-network connectivity between healthy subjects and patients were investigated using FMRIB Software Library (FSL) tools (Melodic ICA, dual regression, FSLNets).

RESULTS

Twenty-three ICA maps were identified as components of neuronal origin. For intra-network connectivity changes, eight components showed a significant connectivity increase in patients (p < 0.05); these were correlated with clinical scores and were largest for (sensori)motor networks. For inter-network connectivity changes, we found higher connectivity between the sensorimotor network and the spatial attention network (p = 0.0098) and lower connectivity between anterior and posterior default mode networks (DMN) (p =  0.024), anterior DMN and visual recognition networks (p = 0.026), as well as between visual attention and main dorsal attention networks (p = 0.03), for patients as compared to healthy subjects. The area under the Receiver Operating Characteristics (ROC) curve for the best predictor (partial correlation between sensorimotor and spatial attention networks) was 0.772. These functional alterations were not associated with any gray or white matter structural changes.

CONCLUSION

Our results show higher connectivity between sensorimotor and spatial attention areas in patients that may be related to the reduced movement automaticity in PD.

Use of 11C-PE2I PET in differential diagnosis of parkinsonian disorders

In idiopathic Parkinson disease and atypical parkinsonian disorders, central dopaminergic and overall brain functional activity are altered to different degrees, causing difficulties in achieving an unambiguous clinical diagnosis. A dual examination using (123)I-FP-CIT ((123)I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane, or (123)I-ioflupane) SPECT and(18)F-FDG PET provides complementary information on dopamine transporter (DAT) availability and overall brain functional activity, respectively. Parametric images based on a single, dynamic (11)C-PE2I (N-(3-iodoprop-2E-enyl)-2β-carbomethoxy-3β-(4-methyl-phenyl)nortropane) scan potentially supply both DAT availability (nondisplaceable binding potential [BPND]) and relative cerebral blood flow (relative delivery [R1]) at voxel level. This study aimed to evaluate the validity of (11)C-PE2I PET against the dual-modality approach using (123)I-FP-CIT SPECT and (18)F-FDG PET.

METHODS

Sixteen patients with parkinsonian disorders had a dual examination with (18)F-FDG PET and (123)I-FP-CIT SPECT following clinical routines and additionally an experimental (11)C-PE2I PET scan. Parametric BPND and R1 images were generated using receptor parametric mapping with the cerebellum as a reference. T1-weighted MR imaging was used for automated definition of volumes of interest (VOI). The DAT VOIs included the basal ganglia, whereas the overall brain functional activity was examined using VOIs across the brain. BPND and R1 values were compared with normalized (123)I-FP-CIT and (18)F-FDG uptake values, respectively, using Pearson correlations and regression analyses. In addition, 2 masked interpreters evaluated the images visually, in both the routine and the experimental datasets, for comparison of patient diagnoses.

RESULTS

Parametric (11)C-PE2I BPND and R1 images showed high consistency with (123)I-FP-CIT SPECT and (18)F-FDG PET images. Correlations between (11)C-PE2I BPND and (123)I-FP-CIT uptake ratios were 0.97 and 0.76 in the putamen and caudate nucleus, respectively. Regional (11)C-PE2I R1 values were moderately to highly correlated with normalized (18)F-FDG values (range, 0.61-0.94). Visual assessment of DAT availability showed a high consistency between (11)C-PE2I BPND and (123)I-FP-CIT images, whereas the consistency was somewhat lower for appraisal of overall brain functional activity using (123)I-FP-CIT and (18)F-FDG images. Substantial differences were found between clinical diagnosis and both neuroimaging diagnoses.

CONCLUSION

A single, dynamic (11)C-PE2I PET investigation is a powerful alternative to a dual examination with (123)I-FP-CIT SPECT and (18)F-FDG PET for differential diagnosis of parkinsonian disorders. A large-scale patient study is, however, needed to further investigate distinct pathologic patterns in overall brain functional activity for various parkinsonian disorders.

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.

Nigrostriatal dopamine terminal imaging with dopamine transporter SPECT: an update

This article gives an update on nigrostriatal dopamine terminal imaging, with emphasis on SPECT performed with the presynaptic dopamine transporter (DAT) ligand (123)I-FP-CIT. The paper covers the rational use of this technique in the diagnostic work-up of patients with known or suspected parkinsonian syndromes. In detail, it addresses the impact of the method for the proof or exclusion of neurodegenerative parkinsonism, for its early and preclinical diagnosis, and for the evaluation of disease progression. The importance of normal DAT binding for differentiating symptomatic parkinsonism and relevant tremor syndromes from neurodegeneration is highlighted. Particularly emphasized is the role of DAT SPECT for diagnosing Lewy body dementia and its separation from Alzheimer dementia. Finally, some remarks deal with the economic aspects of the use of these imaging techniques in the clinical setting.

Transcranial sonography and functional imaging in glucocerebrosidase mutation Parkinson disease

BACKGROUND

Heterozygous glucocerebrosidase (GBA) mutations are the leading genetic risk factor for Parkinson disease, yet imaging correlates, particularly transcranial sonography, have not been extensively described.

METHODS

To determine whether GBA mutation heterozygotes with Parkinson disease demonstrate hyperechogenicity of the substantia nigra, transcranial sonography was performed in Ashkenazi Jewish Parkinson disease subjects, tested for the eight most common Gaucher disease mutations and the LRRK2 G2019S mutation, and in controls. [(18)F]-fluorodeoxyglucose or [(18)F]-fluorodopa positron emission tomography is also reported from a subset of Parkinson disease subjects with heterozygous GBA mutations.

RESULTS

Parkinson disease subjects with heterozygous GBA mutations (n = 23) had a greater median maximal area of substantia nigral echogenicity compared to controls (n = 34, aSNmax = 0.30 vs. 0.18, p = 0.007). There was no difference in median maximal area of nigral echogenicity between Parkinson disease groups defined by GBA and LRRK2 genotype: GBA heterozygotes; GBA homozygotes/compound heterozygotes (n = 4, aSNmax = 0.27); subjects without LRRK2 or GBA mutations (n = 32, aSNmax = 0.27); LRRK2 heterozygotes/homozygotes without GBA mutations (n = 27, aSNmax = 0.28); and GBA heterozygotes/LRRK2 heterozygotes (n = 4, aSNmax = 0.32, overall p = 0.63). In secondary analyses among Parkinson disease subjects with GBA mutations, maximal area of nigral echogenicity did not differ based on GBA mutation severity or mutation number. [(18)F]-fluorodeoxyglucose (n = 3) and [(18)F]-fluorodopa (n = 2) positron emission tomography in Parkinson disease subjects with heterozygous GBA mutations was consistent with findings in idiopathic Parkinson disease.

CONCLUSIONS

Both transcranial sonography and positron emission tomography are abnormal in GBA mutation associated Parkinson disease, similar to other Parkinson disease subjects.

Using imaging to identify psychogenic parkinsonism before deep brain stimulation surgery

The frequency with which patients with atypical parkinsonism and advanced motor symptoms undergo deep brain stimulation (DBS) procedures is unknown. However, the potential exposure of these patients to unnecessary surgical risks makes their identification critical. As many as 15% of patients enrolled in recent early Parkinson disease (PD) trials have been found to lack evidence of a dopaminergic deficit following PET or SPECT imaging. This suggests that a number of patients with parkinsonism who are referred for DBS may not have idiopathic PD. The authors report on 2 patients with probable psychogenic parkinsonism who presented for DBS surgery. They found that both patients had normal caudate and putamen [18F]-fluorodopa uptake on PET imaging, along with normal expression of specific disease-related metabolic networks for PD and multiple system atrophy, a common form of atypical neurodegenerative parkinsonism. The clinical and PET findings in these patients highlight the role of functional imaging in assisting clinical decision making when the diagnosis is uncertain.

Parkinson's disease

Parkinson's disease (PD) is the most common age-related motoric neurodegenerative disease initially described in the 1800's by James Parkinson as the 'Shaking Palsy'. Loss of the neurotransmitter dopamine was recognized as underlying the pathophysiology of the motor dysfunction; subsequently discovery of dopamine replacement therapies brought substantial symptomatic benefit to PD patients. However, these therapies do not fully treat the clinical syndrome nor do they alter the natural history of this disorder motivating clinicians and researchers to further investigate the clinical phenotype, pathophysiology/pathobiology and etiology of this devastating disease. Although the exact cause of sporadic PD remains enigmatic studies of familial and rare toxicant forms of this disorder have laid the foundation for genome wide explorations and environmental studies. The combination of methodical clinical evaluation, systematic pathological studies and detailed genetic analyses have revealed that PD is a multifaceted disorder with a wide-range of clinical symptoms and pathology that include regions outside the dopamine system. One common thread in PD is the presence of intracytoplasmic inclusions that contain the protein, α-synuclein. The presence of toxic aggregated forms of α-synuclein (e.g., amyloid structures) are purported to be a harbinger of subsequent pathology. In fact, PD is both a cerebral amyloid disease and the most common synucleinopathy, that is, diseases that display accumulations of α-synuclein. Here we present our current understanding of PD etiology, pathology, clinical symptoms and therapeutic approaches with an emphasis on misfolded α-synuclein.

Functional neuroimaging in Parkinson's disease

INTRODUCTION

Functional neuroimaging techniques have greatly contributed to improving our understanding of Parkinson's disease (PD) neurodegeneration and related compensatory mechanisms.

AREAS COVERED

In this paper, the authors analyze the role of functional neuroimaging as a diagnostic tool in PD and review functional neuroimaging studies on PD progression and compensatory adaptations. Through this, the article provides the reader with sensible approaches for the use of functional neuroimaging in the diagnosis of PD. The reader is also provided with knowledge on the time course of nigrostriatal dopamine dysfunction in PD as well as an overview of the potential beneficial and deleterious effects of increased dopamine turnover. Finally, the reader is provided with a critical discussion of the differential effects of levodopa and dopamine agonists on presynaptic dopamine markers and the implications for the interpretation of clinical trials.

EXPERT OPINION

Functional neuroimaging probably plays a limited role in the diagnosis of PD. Parkinson's disease pathology leads to an exponential decline in nigrostriatal dopamine function and a compensatory increase in dopamine turnover, which may help delay symptom onset. On the negative side, increased dopamine turnover contributes to the development of treatment-related motor complications. Presynaptic markers of dopamine function are subject to regulatory changes, compromising the direct interpretation of neuroimaging results in trials of neuroprotective therapies for PD.

Neuroimaging in Parkinson's disease

Parkinson's disease (PD) is a common disorder in which the primary features can be related to dopamine deficiency. Changes on structural imaging are limited, but a wealth of abnormalities can be detected using positron emission tomography, single photon emission computed tomography, or functional magnetic resonance imaging to detect changes in neurochemical pathology or functional connectivity. The changes detected on these studies may reflect the disease process itself and/or compensatory responses to the disease, or they may arise in association with disease- and/or treatment-related complications. This review will focus mainly on neurochemical and metabolic studies and reviews various approaches to the assessment of dopaminergic function as well as the function of other neurotransmitters that may be affected in PD. A number of clinical applications are highlighted, including diagnostic utility, identification of preclinical disease, changes associated with motor and nonmotor complications of PD, and the effects of various therapeutic interventions.

Metabolic networks for assessment of therapy and diagnosis in Parkinson's disease

Neuroimaging and modern computational techniques like spatial covariance analysis have contributed greatly to the understanding of neural system abnormalities in neurodegenerative disorders such as Parkinson's disease (PD). The application of network analysis to metabolic PET data obtained from patients with PD has led to the identification and validation of two distinct spatial covariance patterns associated with the motor and cognitive manifestations of the disease. Quantifying the activity of these patterns in individual subjects has provided an objective tool for the assessment of treatment efficacy and differential diagnosis. We have found that activity of the PD motor-related network is modulated by antiparkinsonian treatments such as dopaminergic therapy, deep brain stimulation (DBS), and subthalamic nucleus (STN) gene therapy. By contrast, the cognitive-related network is not altered by these interventions for PD motor symptoms. This pattern may however change in response to therapies targeting the cognitive symptoms of this disorder. Recent work has focused on the identification of specific network biomarkers for atypical parkinsonian conditions such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). These disease-related patterns can potentially be used in an automated imaging-based algorithm to classify patients with these disorders.

Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis

BACKGROUND

Idiopathic Parkinson's disease can present with symptoms similar to those of multiple system atrophy or progressive supranuclear palsy. We aimed to assess whether metabolic brain imaging combined with spatial covariance analysis could accurately discriminate patients with parkinsonism who had different underlying disorders.

METHODS

Between January, 1998, and December, 2006, patients from the New York area who had parkinsonian features but uncertain clinical diagnosis had fluorine-18-labelled-fluorodeoxyglucose-PET at The Feinstein Institute for Medical Research. We developed an automated image-based classification procedure to differentiate individual patients with idiopathic Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. For each patient, the likelihood of having each of the three diseases was calculated by use of multiple disease-related patterns with logistic regression and leave-one-out cross-validation. Each patient was classified according to criteria defined by receiver-operating-characteristic analysis. After imaging, patients were assessed by blinded movement disorders specialists for a mean of 2.6 years before a final clinical diagnosis was made. The accuracy of the initial image-based classification was assessed by comparison with the final clinical diagnosis.

FINDINGS

167 patients were assessed. Image-based classification for idiopathic Parkinson's disease had 84% sensitivity, 97% specificity, 98% positive predictive value (PPV), and 82% negative predictive value (NPV). Imaging classifications were also accurate for multiple system atrophy (85% sensitivity, 96% specificity, 97% PPV, and 83% NPV) and progressive supranuclear palsy (88% sensitivity, 94% specificity, 91% PPV, and 92% NPV).

INTERPRETATION

Automated image-based classification has high specificity in distinguishing between parkinsonian disorders and could help in selecting treatment for early-stage patients and identifying participants for clinical trials.

FUNDING

National Institutes of Health and General Clinical Research Center at The Feinstein Institute for Medical Research.

FDG PET in the Evaluation of Parkinson's Disease

Network analysis of (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is an innovative approach for the study of in movement disorders, such as Parkinson's disease (PD). Spatial covariance analysis of imaging data acquired from PD patients has revealed characteristic regional patterns associated with the motor and cognitive features of disease. Quantification of pattern expression in individual patients can be used for diagnosis, assessment of disease severity, and evaluation of novel medical and surgical therapies. Identification of disease-specific patterns in other parkinsonian syndromes, such as multiple system atrophy and progressive supranuclear palsy, has improved diagnostic accuracy in patients with difficult to diagnose parkinsonism. Further developments of these techniques are likely to enhance the role of functional imaging in investigating underlying abnormalities and potential new therapies in these neurodegenerative diseases.

Simple ratio analysis of 18F-fluorodopa uptake in striatal subregions separates patients with early Parkinson disease from healthy controls

6-(18)F-fluoro-l-dopa ((18)F-FDOPA) is widely used to investigate dopaminergic hypofunction, for instance, in Parkinson disease (PD). Conventionally, a 90-min scan with either a graphical or a metabolite-purified plasma input approach has been used for quantification. In the clinical setting, to increase compliance, especially in patients with more advanced disease, and to increase the efficacy of tracer and scanner time use, a shorter acquisition and a simple quantitative analysis are desirable. Taking into account the asymmetry of clinical symptoms and the uneven distribution of striatal dopaminergic hypofunction may also improve the use of (18)F-FDOPA PET in early disease detection. Therefore, we compared subregional striatal (18)F-FDOPA PET data from a large group of nonmedicated patients with early PD and a set of healthy elderly volunteers to find out whether a simple ratio approach would reliably separate PD patients from healthy controls.

METHODS

A total of 89 nonmedicated patients with early PD and 21 healthy volunteers were studied with (18)F-FDOPA PET, and both a region-to-reference (striatal-to-occipital) ratio (SOR) calculated from 75 to 90 min after injection and a graphical analysis of data calculated from 15 to 90 min after (18)F-FDOPA injection (yielding the influx constant [K(i)(ref)]) were used.

RESULTS

Both SOR and K(i)(ref) values in the PD patients were lowest, relative to those in the healthy controls, in the posterior putamen contralateral to the side with predominant clinical symptoms. The contralateral posterior putamen showed the largest areas under the receiver operating characteristic (ROC) curve-0.994 for SOR and 0.998 for K(i)(ref)-indicating excellent separation of the PD and control groups. The caudate nucleus and the ventral striatum were less impressive in this respect.

CONCLUSION

A single 15-min scan 75 min after tracer injection seems to be sufficient for separating patients with PD from healthy controls in a clinical research environment. This method represents a powerful and economical alternative for research on the disease mechanism and differential diagnosis.

The search for biomarkers in Parkinson's disease: a critical review

Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily presents with features of bradykinesia, rigidity and tremor, and has, as part of its core pathology, the degeneration of dopaminergic neurons in the substantia nigra pars compacta. There is a great need for the development of a reliable diagnostic tool to improve promptness of diagnosis, definition of disease subtypes, and to monitor disease progression and demonstrate treatment efficacy in the case of disease modifying therapies. Current biomarkers range from objective clinical tools, to neuroimaging, to 'wet' markers involving blood and cerebrospinal fluid. To date, all candidate biomarkers for PD have failed to be developed into a clinically useful tool. Ideally, a combination of sensitive markers will be needed, not only to predict the onset of PD, but also to help in subtype classification and to follow progression. Here, we critically review various PD biomarker studies.

Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features

In the current paper, we describe methodologies for single subject differential diagnosis of degenerative brain disorders using multivariate principal component analysis (PCA) of functional imaging scans. An automated routine utilizing these methods is applied to positron emission tomography (PET) brain data to distinguish several discrete parkinsonian movement disorders with similar clinical manifestations. Disease specific expressions of voxel-based spatial covariance patterns are predetermined using the Scaled Subprofile Model (SSM/PCA) and a scalar measure of the manifestation of each pattern in prospective subject images is subsequently derived. Scores are automatically compared to reference values generated for each pathological condition in a corresponding set of patient and control scans. Diagnostic outcome is optimized using strategies such as the derivation of patterns in a voxel subspace that reflects contrasting image characteristics between conditions, or by using an independent patient population as controls. The prediction models for two, three and four way classification problems using direct scalar comparison as well as classical discriminant analysis are assessed in a composite training population comprised of three different patient classes and normal controls, and validated in a similar independent test population. Results illustrate that highly accurate diagnosis can often be achieved by simple comparison of scores utilizing optimized patterns.

Normalization in PET group comparison studies--the importance of a valid reference region

INTRODUCTION

In positron emission tomography (PET) studies of cerebral blood flow (CBF) and metabolism, the large interindividual variation commonly is minimized by normalization to the global mean prior to statistical analysis. This approach requires that no between-group or between-state differences exist in the normalization region. Given the variability typical of global CBF and the practical limit on sample size, small group differences in global mean easily elude detection, but still bias the comparison, with profound consequences for the physiological interpretation of the results.

MATERIALS AND METHODS

Quantitative [15O]H2O PET recordings of CBF were obtained in 45 healthy subjects (21-81 years) and 14 patients with hepatic encephalopathy (HE). With volume-of-interest (VOI) and voxel-based statistics, we conducted regression analyses of CBF as function of age in the healthy group, and compared the HE group to a subset of the controls. We compared absolute CBF values, and CBF normalized to the gray matter (GM) and white matter (WM) means. In additional simulation experiments, we manipulated the cortical values of 12 healthy subjects and compared these to unaltered control data.

RESULTS

In healthy aging, CBF was shown to be unchanged in WM and central regions. In contrast, with normalization to the GM mean, CBF displayed positive correlation with age in the central regions. Very similar artifactual increases were seen in the HE comparison and also in the simulation experiment.

CONCLUSION

Ratio normalization to the global mean readily elevates CBF in unchanged regions when a systematic between-group difference exists in gCBF, also when this difference is below the detection threshold. We suggest that the routine normalization to the global mean in earlier studies resulted in spurious interpretations of perturbed CBF. Normalization to central WM yields less biased results in aging and HE and could potentially serve as a normalization reference region in other disorders as well.

Assessment of the progression of Parkinson's disease: a metabolic network approach

BACKGROUND

Clinical research into Parkinson's disease has focused increasingly on the development of interventions that slow the neurodegeneration underlying this disorder. These investigations have stimulated interest in finding objective biomarkers that show changes in the rate of disease progression with treatment. Through radiotracer-based imaging of nigrostriatal dopaminergic function, a specific class of biomarkers to monitor the progression of Parkinson's disease has been identified, and these biomarkers were used in the clinical trials of drugs with the potential to modify the course of the disease. However, in some of these studies there was discordance between the imaging outcome measures and blinded clinical ratings of disease severity. Research is underway to identify and validate alternative ways to image brain metabolism, through which the efficacy of new therapies for Parkinson's disease and related disorders can be assessed.

RECENT DEVELOPMENTS

During recent years, spatial covariance analysis has been used with (18)F-fluorodeoxyglucose PET to detect abnormal patterns of brain metabolism in patients with neurodegenerative disorders. Rapid, automated, voxel-based algorithms have been used with metabolic imaging to quantify the activity of disease-specific networks. This approach has helped to characterise the unique metabolic patterns associated with the motor and cognitive features of Parkinson's disease. The results of several studies have shown correction of abnormal motor, but not cognitive, network activity by treatment with dopaminergic therapy and deep brain stimulation. The authors of a longitudinal imaging study of early-stage Parkinson's disease reported substantial differences in the development of these metabolic networks over a follow-up of 4 years. WHERE NEXT?: Developments in network imaging have provided the basis for several new applications of metabolic imaging in the study of Parkinson's disease. A washout study is currently underway to determine the long-duration effects of dopaminergic therapy on the network activity related to Parkinson's disease, which will be useful to plan future trials of disease-modifying drugs. Network approaches are also being applied to the study of atypical parkinsonian syndromes. The characterisation of specific patterns associated with atypical parkinsonian syndromes and classic Parkinson's disease will be the basis for a fully automated imaging-based procedure for early differential diagnosis. Efforts are underway to quantify the networks related to Parkinson's disease with less invasive imaging methods. Assessments of network activity with perfusion-weighted MRI show excellent concordance with measurements done with established radiotracer techniques. This approach will ultimately enable the assessment of abnormal network activity in people who are genetically at risk of Parkinson's disease.