Chris C. Tang

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

BACKGROUND Idiopathic 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. 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 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. FINDINGS 167 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). 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.

Abnormalities in Metabolic Network Activity Precede the Onset of Motor Symptoms in Parkinson's Disease

Imaging studies show that Parkinsons disease (PD) alters the activity of motor- and cognition-related metabolic brain networks. However, it is not known whether the network changes appear at or before symptom onset. In this study, we examined 15 hemiparkinsonian patients who underwent serial metabolic imaging with [18F]-fluorodeoxyglucose (FDG) PET at baseline and again 2.1 ± 0.6 (mean ± SD) and 3.9 ± 0.7 years later. We assessed longitudinal changes in network activity in each cerebral hemisphere, focusing specifically on the “presymptomatic” hemisphere—ipsilateral to the initially involved body side. At the network level, the activity of the PD motor-related pattern (PDRP) increased symmetrically in both hemispheres over time (p < 0.001), with significant bilateral elevations at each of the three time points. Hemispheric expression of the PD cognition-related pattern likewise increased symmetrically (p < 0.001), although significant elevations were not evident on either side until 4 years. At the regional level, putamen metabolism contralateral to the initially affected body side was elevated at all three time points, without longitudinal change. In contrast, in the initially presymptomatic hemisphere, putamen metabolic activity increased steadily over time, reaching abnormal levels only at 4 years. Metabolic activity in the contralateral precuneus fell to subnormal levels by the final time point. These findings suggest that abnormal PDRP activity antecedes the appearance of motor signs by ∼2 years. The timing and laterality of symptom onset relates to focal asymmetric metabolic changes at the putamenal node of this network.

Abnormal metabolic network activity in REM sleep behavior disorder.

Objective: To determine whether the Parkinson disease–related covariance pattern (PDRP) expression is abnormally increased in idiopathic REM sleep behavior disorder (RBD) and whether increased baseline activity is associated with greater individual risk of subsequent phenoconversion. Methods: For this cohort study, we recruited 2 groups of RBD and control subjects. Cohort 1 comprised 10 subjects with RBD (63.5 ± 9.4 years old) and 10 healthy volunteers (62.7 ± 8.6 years old) who underwent resting-state metabolic brain imaging with 18F-fluorodeoxyglucose PET. Cohort 2 comprised 17 subjects with RBD (68.9 ± 4.8 years old) and 17 healthy volunteers (66.6 ± 6.0 years old) who underwent resting brain perfusion imaging with ethylcysteinate dimer SPECT. The latter group was followed clinically for 4.6 ± 2.5 years by investigators blinded to the imaging results. PDRP expression was measured in both RBD groups and compared with corresponding control values. Results: PDRP expression was elevated in both groups of subjects with RBD (cohort 1: p < 0.04; cohort 2: p < 0.005). Of the 17 subjects with long-term follow-up, 8 were diagnosed with Parkinson disease or dementia with Lewy bodies; the others did not phenoconvert. For individual subjects with RBD, final phenoconversion status was predicted using a logistical regression model based on PDRP expression and subject age at the time of imaging (r2 = 0.64, p < 0.0001). Conclusions: Latent network abnormalities in subjects with idiopathic RBD are associated with a greater likelihood of subsequent phenoconversion to a progressive neurodegenerative syndrome.

Metabolic network as a progression biomarker of premanifest Huntington's disease

BACKGROUND The evaluation of effective disease-modifying therapies for neurodegenerative disorders relies on objective and accurate measures of progression in at-risk individuals. Here we used a computational approach to identify a functional brain network associated with the progression of preclinical Huntingtons disease (HD). METHODS Twelve premanifest HD mutation carriers were scanned with [18F]-fluorodeoxyglucose PET to measure cerebral metabolic activity at baseline and again at 1.5, 4, and 7 years. At each time point, the subjects were also scanned with [11C]-raclopride PET and structural MRI to measure concurrent declines in caudate/putamen D2 neuroreceptor binding and tissue volume. The rate of metabolic network progression in this cohort was compared with the corresponding estimate obtained in a separate group of 21 premanifest HD carriers who were scanned twice over a 2-year period. RESULTS In the original premanifest cohort, network analysis disclosed a significant spatial covariance pattern characterized by progressive changes in striato-thalamic and cortical metabolic activity. In these subjects, network activity increased linearly over 7 years and was not influenced by intercurrent phenoconversion. The rate of network progression was nearly identical when measured in the validation sample. Network activity progressed at approximately twice the rate of single region measurements from the same subjects. CONCLUSION Metabolic network measurements provide a sensitive means of quantitatively evaluating disease progression in premanifest individuals. This approach may be incorporated into clinical trials to assess disease-modifying agents. TRIAL REGISTRATION Registration is not required for observational studies. FUNDING NIH (National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering) and CHDI Foundation Inc.

Metabolic resting-state brain networks in health and disease

Significance We present an innovative approach to evaluate default mode network (DMN) activity in individual subjects using metabolic imaging. After characterizing a distinct set of metabolic resting state networks (RSNs) in healthy subjects, network activity was tracked over time in patients with neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease. We found that the dominant normal metabolic RSN, which corresponded to the DMN, is preserved in early-stage Parkinson’s disease patients. Although significant DMN reductions developed later, these changes were reversible in part by dopamine treatment. This finding contrasts with Alzheimer’s disease, in which DMN loss is rapid and continuous, beginning before clinical diagnosis. Metabolic imaging can provide a versatile, quantitative means of assessing brain disease at the network level. The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson’s disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the “DMN-like” dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer’s disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer’s disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

Improved Sequence Learning with Subthalamic Nucleus Deep Brain Stimulation: Evidence for Treatment-Specific Network Modulation

We used a network approach to study the effects of anti-parkinsonian treatment on motor sequence learning in humans. Eight Parkinsons disease (PD) patients with bilateral subthalamic nucleus (STN) deep brain stimulation underwent H215O positron emission tomography (PET) imaging to measure regional cerebral blood flow (rCBF) while they performed kinematically matched sequence learning and movement tasks at baseline and during stimulation. Network analysis revealed a significant learning-related spatial covariance pattern characterized by consistent increases in subject expression during stimulation (p = 0.008, permutation test). The network was associated with increased activity in the lateral cerebellum, dorsal premotor cortex, and parahippocampal gyrus, with covarying reductions in the supplementary motor area (SMA) and orbitofrontal cortex. Stimulation-mediated increases in network activity correlated with concurrent improvement in learning performance (p < 0.02). To determine whether similar changes occurred during dopaminergic pharmacotherapy, we studied the subjects during an intravenous levodopa infusion titrated to achieve a motor response equivalent to stimulation. Despite consistent improvement in motor ratings during infusion, levodopa did not alter learning performance or network activity. Analysis of learning-related rCBF in network regions revealed improvement in baseline abnormalities with STN stimulation but not levodopa. These effects were most pronounced in the SMA. In this region, a consistent rCBF response to stimulation was observed across subjects and trials (p = 0.01), although the levodopa response was not significant. These findings link the cognitive treatment response in PD to changes in the activity of a specific cerebello-premotor cortical network. Selective modulation of overactive SMA–STN projection pathways may underlie the improvement in learning found with stimulation.

A disease-specific metabolic brain network associated with corticobasal degeneration

Corticobasal degeneration is an uncommon parkinsonian variant condition that is diagnosed mainly on clinical examination. To facilitate the differential diagnosis of this disorder, we used metabolic brain imaging to characterize a specific network that can be used to discriminate corticobasal degeneration from other atypical parkinsonian syndromes. Ten non-demented patients (eight females/two males; age 73.9 ± 5.7 years) underwent metabolic brain imaging with (18)F-fluorodeoxyglucose positron emission tomography for atypical parkinsonism. These individuals were diagnosed clinically with probable corticobasal degeneration. This diagnosis was confirmed in the three subjects who additionally underwent post-mortem examination. Ten age-matched healthy subjects (five females/five males; age 71.7 ± 6.7 years) served as controls for the imaging studies. Spatial covariance analysis was applied to scan data from the combined group to identify a significant corticobasal degeneration-related metabolic pattern that discriminated (P < 0.001) the patients from the healthy control group. This pattern was characterized by bilateral, asymmetric metabolic reductions involving frontal and parietal cortex, thalamus, and caudate nucleus. These pattern-related changes were greater in magnitude in the cerebral hemisphere opposite the more clinically affected body side. The presence of this corticobasal degeneration-related metabolic topography was confirmed in two independent testing sets of patient and control scans, with elevated pattern expression (P < 0.001) in both disease groups relative to corresponding normal values. We next determined whether prospectively computed expression values for this pattern accurately discriminated corticobasal degeneration from multiple system atrophy and progressive supranuclear palsy (the two most common atypical parkinsonian syndromes) on a single case basis. Based upon this measure, corticobasal degeneration was successfully distinguished from multiple system atrophy (P < 0.001) but not progressive supranuclear palsy, presumably because of the overlap (∼ 24%) that existed between the corticobasal degeneration- and the progressive supranuclear palsy-related metabolic topographies. Nonetheless, excellent discrimination between these disease entities was achieved by computing hemispheric asymmetry scores for the corticobasal degeneration-related pattern on a prospective single scan basis. Indeed, a logistic algorithm based on the asymmetry scores combined with separately computed expression values for a previously validated progressive supranuclear palsy-related pattern provided excellent specificity (corticobasal degeneration: 92.7%; progressive supranuclear palsy: 94.1%) in classifying 58 testing subjects. In conclusion, corticobasal degeneration is associated with a reproducible disease-related metabolic covariance pattern that may help to distinguish this disorder from other atypical parkinsonian syndromes.

Decreased Firing of Striatal Neurons Related to Licking During Acquisition and Overtraining of a Licking Task

Neurons that fire in relation to licking, in the ventral part of the dorsolateral striatum (DLS), were studied during acquisition and performance of a licking task in rats for 14 sessions (2 h/d). Task learning was indicated by fewer errors of omission of licking and improved movement efficiency (i.e., shorter lick duration) over sessions. Number of licks did not change over sessions. Overtraining did not result in habit formation, as indicated by similar reductions of licking responses following devaluation by satiety in both early and late sessions. Twenty-nine lick neurons recorded and tracked over sessions exhibited a significant linear decrease in average firing rate across all neurons over sessions, correlating with concurrent declines in lick duration. Individually, most neurons (86%) exhibited decreased firing rates, while a small proportion (14%) exhibited increased firing rates, during lick movements that were matched over sessions. Reward manipulations did not alter firing patterns over sessions. Aside from the absence of habit formation, striatal processing during unconditioned movements (i.e., licking) was characterized by high activity of movement-related neurons during early performance and decreased activity of the same neurons during overtraining, similar to our previous report of head movement neurons during acquired, skilled, instrumental head movements that ultimately became habitual (Tang et al., 2007). Decreased activity in DLS neurons may reflect a common neural mechanism underlying improvement in movement efficiency with overtraining. Nonetheless, the decreased striatal firing in relation to a movement that did not become habitual demonstrates that not all DLS changes reflect habit formation.

Parkinson's Disease: Increased Motor Network Activity in the Absence of Movement

We used a network approach to assess systems-level abnormalities in motor activation in humans with Parkinsons disease (PD). This was done by measuring the expression of the normal movement-related activation pattern (NMRP), a previously validated activation network deployed by healthy subjects during motor performance. In this study, NMRP expression was prospectively quantified in 15O-water PET scans from a PD patient cohort comprised of a longitudinal early-stage group (n = 12) scanned at baseline and at two or three follow-up visits two years apart, and a moderately advanced group scanned on and off treatment with either subthalamic nucleus deep brain stimulation (n = 14) or intravenous levodopa infusion (n = 14). For each subject and condition, we measured NMRP expression during both movement and rest. Resting expression of the abnormal PD-related metabolic covariance pattern was likewise determined in the same subjects. NMRP expression was abnormally elevated (p < 0.001) in PD patients scanned in the nonmovement rest state. By contrast, network activity measured during movement did not differ from normal (p = 0.34). In the longitudinal cohort, abnormal increases in resting NMRP expression were evident at the earliest clinical stages (p < 0.05), which progressed significantly over time (p = 0.003). Analogous network changes were present at baseline in the treatment cohort (p = 0.001). These abnormalities improved with subthalamic nucleus stimulation (p < 0.005) but not levodopa (p = 0.25). In both cohorts, the changes in NMRP expression that were observed did not correlate with concurrent PD-related metabolic covariance pattern measurements (p > 0.22). Thus, the resting state in PD is characterized by changes in the activity of normal as well as pathological brain networks.

Automated Differential Diagnosis of Early Parkinsonism using Metabolic Brain Networks: A Validation Study

The differentiation of idiopathic Parkinson disease (IPD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), the most common atypical parkinsonian look-alike syndromes (APS), can be clinically challenging. In these disorders, diagnostic inaccuracy is more frequent early in the clinical course when signs and symptoms are mild. Diagnostic inaccuracy may be particularly relevant in trials of potential disease-modifying agents, which typically involve participants with early clinical manifestations. In an initial study, we developed a probabilistic algorithm to classify subjects with clinical parkinsonism but uncertain diagnosis based on the expression of metabolic covariance patterns for IPD, MSA, and PSP. Classifications based on this algorithm agreed closely with final clinical diagnosis. Nonetheless, blinded prospective validation is required before routine use of the algorithm can be considered. Methods: We used metabolic imaging to study an independent cohort of 129 parkinsonian subjects with uncertain diagnosis; 77 (60%) had symptoms for 2 y or less at the time of imaging. After imaging, subjects were followed by blinded movement disorders specialists for an average of 2.2 y before final diagnosis was made. When the algorithm was applied to the individual scan data, the probabilities of IPD, MSA, and PSP were computed and used to classify each of the subjects. The resulting image-based classifications were then compared with the final clinical diagnosis. Results: IPD subjects were distinguished from APS with 94% specificity and 96% positive predictive value (PPV) using the original 2-level logistic classification algorithm. The algorithm achieved 90% specificity and 85% PPV for MSA and 94% specificity and 94% PPV for PSP. The diagnostic accuracy was similarly high (specificity and PPV > 90%) for parkinsonian subjects with short symptom duration. In addition, 25 subjects were classified as level I indeterminate parkinsonism and 4 more subjects as level II indeterminate APS. Conclusion: Automated pattern-based image classification can improve the diagnostic accuracy in patients with parkinsonism, even at early disease stages.