Thomas Eckert

FDG PET in the differential diagnosis of parkinsonian disorders

The differential diagnosis of parkinsonian disorders can be challenging, especially early in the disease course. PET imaging with [(18)F]-fluorodeoxyglucose (FDG) has been used to identify characteristic patterns of regional glucose metabolism in patient cohorts with idiopathic Parkinsons disease (PD), as well as variant forms of parkinsonism such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBGD). In this study, we assessed the utility of FDG PET in the differential diagnosis of individual patients with clinical parkinsonism. 135 parkinsonian patients were referred for FDG PET to determine whether their diagnosis could be made accurately based upon their scans. Imaging-based diagnosis was obtained by visual assessment of the individual scans and also by computer-assisted interpretation. The results were compared with 2-year follow-up clinical assessments made by independent movement disorders specialists who were blinded to the original PET findings. We found that blinded computer assessment agreed with clinical diagnosis in 92.4% of all subjects (97.7% early PD, 91.6% late PD, 96% MSA, 85% PSP, 90.1% CBGD, 86.5% healthy control subjects). Concordance of visual inspection with clinical diagnosis was achieved in 85.4% of the patients scanned (88.4% early PD, 97.2% late PD, 76% MSA, 60% PSP, 90.9% CBGD, 90.9% healthy control subjects). This study demonstrates that FDG PET performed at the time of initial referral for parkinsonism accurately predicted the clinical diagnosis of individual patients made at subsequent follow-up. Computer-assisted methodologies may be particularly helpful in situations where experienced readers of FDG PET images are not readily available.

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

BACKGROUNDnIdiopathic Parkinsons 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.nnnMETHODSnBetween 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 Parkinsons 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.nnnFINDINGSn167 patients were assessed. Image-based classification for idiopathic Parkinsons 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).nnnINTERPRETATIONnAutomated 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.nnnFUNDINGnNational Institutes of Health and General Clinical Research Center at The Feinstein Institute for Medical Research.

Differentiation of idiopathic Parkinson's disease, multiple system atrophy, progressive supranuclear palsy, and healthy controls using magnetization transfer imaging

The differentiation of multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) from idiopathic Parkinsons disease (IPD) is difficult. Magnetization transfer imaging (MTI), a measure that correlates with myelination and axonal density, was employed in this study in the attempt to distinguish between these disorders. Measurements were carried out in 15 patients with IPD, 12 patients with MSA, 10 patients with PSP, and in 20 aged-matched healthy control subjects. The main finding was a change in the magnetization transfer ratio in the globus pallidus, putamen, caudate nucleus, substantia nigra, and white matter in IPD, MSA, and PSP patients, matching the pathological features of the underlying disorder. Furthermore, stepwise linear discriminant analysis provided a good classification of the individual patients into the different disease groups. All IPD patients and control subjects were correctly separated from the MSA and PSP cohort, and all PSP patients and 11 of 12 MSA patients were correctly separated from the IPD and control cohort. There was also a fairly good discrimination of IPD patients from control subjects and of MSA from PSP patients. In conclusion, MTI revealed degenerative changes in patients with different parkinsonian syndromes matching the underlying pathological features of the different diseases, underlining the high potential of this method in distinguishing MSA and PSP from IPD.

Abnormal metabolic networks in atypical parkinsonism

Spatial covariance analysis has been used with 18F‐fluorodeoxyglucose (FDG) PET to detect and quantify specific metabolic patterns associated with Parkinsons disease (PD). However, PD‐related patterns cannot necessarily serve as biomarkers of the processes that underlie the atypical parkinsonian syndromes. In this FDG PET study, we used strictly defined statistical criteria to identify disease‐related metabolic patterns in the imaging data from patients with multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), the two most common of these atypical conditions. We found that MSA and PSP were each associated with a specific, highly stable metabolic brain network (P < 0.0001, bootstrap estimation). The MSA‐related pattern was characterized by decreased metabolism in the putamen and cerebellum. The PSP‐related pattern was characterized by metabolic decreases in the brainstem and medial frontal cortex. For both conditions, pattern expression was significantly elevated in patients relative to age‐matched healthy control subjects (P < 0.001). For each condition, we validated the associated disease‐related metabolic pattern by computing its expression on an individual scan basis in two independent patient cohorts, and in one subsequent healthy volunteer cohort. We found that for both MSA and PSP, prospective assessments of pattern expression accurately discriminated patients from controls (P < 0.001). These findings suggest that the major atypical parkinsonian syndromes are associated with distinct patterns of abnormal regional metabolic activity. These disease‐related networks can potentially be used in conjunction with functional brain imaging as quantifiable biomarkers for the assessment of these pathological conditions.

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

BACKGROUNDnClinical research into Parkinsons 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 Parkinsons 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 Parkinsons disease and related disorders can be assessed.nnnRECENT DEVELOPMENTSnDuring 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 Parkinsons 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 Parkinsons 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 Parkinsons disease. A washout study is currently underway to determine the long-duration effects of dopaminergic therapy on the network activity related to Parkinsons 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 Parkinsons 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 Parkinsons 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 Parkinsons disease.

Neuroimaging and Therapeutics in Movement Disorders

SummaryIn this review, we discuss the role of neuroimaging in assessing treatment options for movement disorders, particularly Parkinson’s disease (PD). Imaging methods to assess dopaminergic function have recently been applied in trials of potential neuroprotective agents. Other imaging methods using regional metabolism and/or cerebral perfusion have been recently introduced to quantify the modulation of network activity as an objective marker of the treatment response. Both imaging strategies have provided novel insights into the mechanisms underlying a variety of pharmacological and stereotaxic surgical treatment strategies for PD and other movement disorders.

Quantification of Parkinson's disease-related network expression with ECD SPECT.

PurposeSpatial covariance analysis has been used with FDG PET to identify a specific metabolic network associated with Parkinson’s disease (PD). In the current study, we utilized a new, fully automated voxel-based method to quantify network expression in ECD SPECT images from patients with classical PD, patients with multiple system atrophy (MSA), and healthy control subjects.MethodsWe applied a previously validated voxel-based PD-related covariance pattern (PDRP) to quantify network expression in the ECD SPECT scans of 35 PD patients, 15 age- and disease severity-matched MSA patients, and 35 age-matched healthy control subjects. PDRP scores were compared across groups using analysis of variance. The sensitivity and specificity of the prospectively computed PDRP scores in the differential diagnosis of individual subjects were assessed by receiver operating characteristic (ROC) analysis.ResultsPDRP scores were significantly increased (pu2009<u20090.001) in the PD group relative to the MSA and control groups. ROC analysis indicated that the overall diagnostic accuracy of the PDRP measures was 0.91 (AUC). The optimal cutoff value was consistent with a sensitivity of 0.97 and a specificity of 0.80 and 0.71 for discriminating PD patients from MSA and normal controls, respectively.ConclusionOur findings suggest that fully automated voxel-based network assessment techniques can be used to quantify network expression in the ECD SPECT scans of parkinsonian patients.

Regional metabolic changes in Parkinsonian patients with normal dopaminergic imaging

Dopaminergic imaging has been found to be normal in approximately 15% of parkinsonian patients enrolled in neuroprotective trials. We used 18F‐fluorodeoxyglucose positron emission tomography (FDG PET) to determine the metabolic basis for this finding. We reviewed scans from 185 patients with clinical signs of Parkinsons disease (PD) who underwent 18F‐fluorodopa PET imaging for diagnostic confirmation. Of this group, 27 patients (14.6%) had quantitatively normal scans; 8 of these patients were additionally scanned with FDG PET. Pattern analysis was performed on an individual scan basis to determine whether the metabolic changes were consistent with classic PD. Computer‐assisted single‐case assessments of the FDG PET scans of these 8 patients did not disclose patterns of regional metabolic change compatible with classical PD or an atypical parkinsonian variant. Similarly, network quantification revealed that PD‐related pattern expression was not elevated in these patients as it was in an age‐ and duration‐matched cohort with classical PD (P < 0.0001). None of these patients developed clinical signs of classical PD or of an atypical parkinsonian syndrome at a follow‐up visit conducted 3 years after imaging. The results suggest that parkinsonian subjects with normal dopaminergic imaging do not have evidence of classical PD or an atypical parkinsonian syndrome.

Network correlates of disease severity in multiple system atrophy

Objective: Multiple system atrophy (MSA), the most common of the atypical parkinsonian disorders, is characterized by the presence of an abnormal spatial covariance pattern in resting state metabolic brain images from patients with this disease. Nonetheless, the potential utility of this pattern as a MSA biomarker is contingent upon its specificity for this disorder and its relationship to clinical disability in individual patients. Methods: We used [18F]fluorodeoxyglucose PET to study 33 patients with MSA, 20 age- and severity-matched patients with idiopathic Parkinson disease (PD), and 15 healthy volunteers. For each subject, we computed the expression of the previously characterized metabolic covariance patterns for MSA and PD (termed MSARP and PDRP, respectively) on a prospective single-case basis. The resulting network values for the individual patients were correlated with clinical motor ratings and disease duration. Results: In the MSA group, disease-related pattern (MSARP) values were elevated relative to the control and PD groups (p < 0.001 for both comparisons). In this group, MSARP values correlated with clinical ratings of motor disability (r = 0.57, p = 0.0008) and with disease duration (r = −0.376, p = 0.03). By contrast, MSARP expression in the PD group did not differ from control values (p = 1.0). In this group, motor ratings correlated with PDRP (r = 0.60, p = 0.006) but not with MSARP values (p = 0.88). Conclusions: MSA is associated with elevated expression of a specific disease-related metabolic pattern. Moreover, differences in the expression of this pattern in patients with MSA correlate with clinical disability. The findings suggest that the MSARP may be a useful biomarker in trials of new therapies for this disorder.

The role of functional neuroimaging in the differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy.

Abstract.Parkinsonism is a symptom of a number of neurodegenerativendisorders in the elderly. Even though clinical criteria fornvarious parkinsonian disorders have been developed recently, thendifferential diagnosis of parkinsonian disorders based onnclinical symptoms remains unsatisfactory, particularly in earlyndisease stages. Early differential diagnosis on the other handnis important as prognosis and treatment options differnsubstantially. Multiple system atrophy (MSA) is one of the majorndifferential diagnoses of idiopathic Parkinson’s disease (PD).nRadiotracer-based imaging methods such as positron emissionntomography (PET) remain the established method for differentialndiagnosis of parkinsonian disorders. The following papernprovides a review of different PET imaging methods for thendifferential diagnosis of PD and MSA patients.