[Show abstract][Hide abstract] ABSTRACT: Methods:
We used metabolic imaging to study an independent cohort of 129 parkinsonian subjects with uncertain diagnosis; 77 (60%) had symptoms for 2 years or less at the time of imaging. After imaging, subjects were followed by a blinded movement disorders specialist for an average of 2.2 years before a final diagnosis was made. By applying the algorithm 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 to the final clinical diagnosis.
Using the original two-level logistical classification algorithm, IPD subjects were distinguished from APS with 94% specificity and 96% positive predictive value (PPV). The algorithm achieved 90% specificity and 85% PPV for MSA, and 94% specificity and 94% PPV for PSP. 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 four more subjects as Level-II Indeterminate APS.
Automated pattern-based image classification can improve diagnostic accuracy in patients with parkinsonism, even at early disease stages.
Journal of Nuclear Medicine 10/2015; DOI:10.2967/jnumed.115.161992 · 6.16 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Although primary dystonia is defined by its characteristic motor manifestations, non-motor signs and symptoms have increasingly been recognized in this disorder. Recent neuroimaging studies have related the motor features of primary dystonia to connectivity changes in cerebello-thalamo-cortical pathways. It is not known, however, whether the non-motor manifestations of the disorder are associated with similar circuit abnormalities. To explore this possibility, we used functional magnetic resonance imaging to study primary dystonia and healthy volunteer subjects while they performed a motion perception task in which elliptical target trajectories were visually tracked on a computer screen. Prior functional magnetic resonance imaging studies of healthy subjects performing this task have revealed selective activation of motor regions during the perception of 'natural' versus 'unnatural' motion (defined respectively as trajectories with kinematic properties that either comply with or violate the two-thirds power law of motion). Several regions with significant connectivity changes in primary dystonia were situated in proximity to normal motion perception pathways, suggesting that abnormalities of these circuits may also be present in this disorder. To determine whether activation responses to natural versus unnatural motion in primary dystonia differ from normal, we used functional magnetic resonance imaging to study 10 DYT1 dystonia and 10 healthy control subjects at rest and during the perception of 'natural' and 'unnatural' motion. Both groups exhibited significant activation changes across perceptual conditions in the cerebellum, pons, and subthalamic nucleus. The two groups differed, however, in their responses to 'natural' versus 'unnatural' motion in these regions. In healthy subjects, regional activation was greater during the perception of natural (versus unnatural) motion (P < 0.05). By contrast, in DYT1 dystonia subjects, activation was relatively greater during the perception of unnatural (versus natural) motion (P < 0.01). To explore the microstructural basis for these functional changes, the regions with significant interaction effects (i.e. those with group differences in activation across perceptual conditions) were used as seeds for tractographic analysis of diffusion tensor imaging scans acquired in the same subjects. Fibre pathways specifically connecting each of the significant functional magnetic resonance imaging clusters to the cerebellum were reconstructed. Of the various reconstructed pathways that were analysed, the ponto-cerebellar projection alone differed between groups, with reduced fibre integrity in dystonia (P < 0.001). In aggregate, the findings suggest that the normal pattern of brain activation in response to motion perception is disrupted in DYT1 dystonia. Thus, it is unlikely that the circuit changes that underlie this disorder are limited to primary sensorimotor pathways.
[Show abstract][Hide abstract] ABSTRACT: Idiopathic rapid eye movement sleep behavior disorder (RBD) is a risk marker for subsequent development of neurodegenerative parkinsonism. In this study, we aimed to investigate whether regional cerebral metabolism is altered in patients with RBD and whether regional metabolic activities are associated with clinical measurements in individual patients. Twenty-one patients with polysomnogram-confirmed RBD and 21 age-matched healthy controls were recruited to undertake positron emission tomography imaging with [(18)F]fluorodeoxyglucose. Differences in normalized regional metabolism and correlations between metabolic activity and clinical indices in RBD patients were evaluated on a voxel basis using statistic parametric mapping analysis. Compared with controls, patients with RBD showed increased metabolism in the hippocampus/parahippocampus, cingulate, supplementary motor area, and pons, but decreased metabolism in the occipital cortex/lingual gyrus (P<0.001). RBD duration correlated with metabolism positively in the anterior vermis (r=0.55, P=0.01), but negatively in the medial frontal gyrus (r=-0.59, P=0.005). In addition, chin electromyographic activity presented a positive metabolic correlation in the hippocampus/parahippocampus (r=0.48, P=0.02), but a negative metabolic correlation in the posterior cingulate (r=-0.61, P=0.002). This study has suggested that region-specific metabolic abnormalities exist in RBD patients and regional metabolic activities are associated with clinical measures such as RBD duration and chin electromyographic activity.Journal of Cerebral Blood Flow & Metabolism advance online publication, 29 July 2015; doi:10.1038/jcbfm.2015.173.
Journal of cerebral blood flow and metabolism: official journal of the International Society of Cerebral Blood Flow and Metabolism 07/2015; 35(11). DOI:10.1038/jcbfm.2015.173 · 5.41 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Atypical parkinsonian syndromes are often difficult to diagnose because they present common clinical symptoms and differences in diagnostic images are subtle. Multivariate covariance analysis has been previously used in PET group data to identify neurodegenerative disease patterns. In particular, using SSM-PCA analysis, individual subject's pattern expression of characteristic disease patterns have been shown to correlate with independent measures of disease status. These scalar subject scores, evaluated as the inner product of the unitized pattern vector and the mean centered subject data vector, can be utilized in classification algorithms to differentiate patients requiring disease specific treatment. However, diagnostic accuracy is often compromised stemming from topographic pattern similarity resulting in overlapping disease score expression. Here, we show that some improvement in classification may be achieved by utilizing the inner product of standardized pattern/patient vectors equivalent to the Pearson's correlation coefficient to evaluate subject class scores.
[Show abstract][Hide abstract] ABSTRACT: Cognitive deficits in Parkinson's disease (PD) have been associated with a specific metabolic covariance pattern. Although the expression of this PD cognition-related pattern (PDCP) correlates with neuropsychological performance, it is not known whether the PDCP topography is reproducible across PD populations. We therefore sought to identify a PDCP topography in a new sample comprised of 19 Dutch PD subjects. Network analysis of metabolic scans from these individuals revealed a significant PDCP that resembled the original network topography. Expression values for the new PDCP correlated (P=0.001) with executive dysfunction on the Frontal Assessment Battery (FAB). Subject scores for the new PDCP correlated (P<0.001) with corresponding values for the original pattern, which also correlated (P<0.005) with FAB scores in this patient group. For further validation, subject scores for the new PDCP were computed in an independent group of 86 American PD patients. In this cohort, subject scores for the new and original PDCP topographies were closely correlated (P<0.001); significant correlations between pattern expression and cognitive performance (P<0.05) were observed for both PDCP topographies. These findings suggest that the PDCP is a replicable imaging marker of PD cognitive dysfunction.Journal of Cerebral Blood Flow & Metabolism advance online publication, 10 June 2015; doi:10.1038/jcbfm.2015.112.
Journal of cerebral blood flow and metabolism: official journal of the International Society of Cerebral Blood Flow and Metabolism 06/2015; 35(9). DOI:10.1038/jcbfm.2015.112 · 5.41 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this study, we sought to identify a disease-related spatial covariance pattern of spontaneous neural activity in Parkinson's disease using resting-state functional magnetic resonance imaging (MRI). Time-series data were acquired in 58 patients with early to moderate stage Parkinson's disease and 54 healthy controls, and analyzed by Scaled Subprofile Model Principal Component Analysis toolbox. A split-sample analysis was also performed in a derivation sample of 28 patients and 28 control subjects and validated in a prospective testing sample of 30 patients and 26 control subjects. The topographic pattern of neural activity in Parkinson's disease was characterized by decreased activity in the striatum, supplementary motor area, middle frontal gyrus, and occipital cortex, and increased activity in the thalamus, cerebellum, precuneus, superior parietal lobule, and temporal cortex. Pattern expression was elevated in the patients compared with the controls, with a high accuracy (90%) to discriminate the patients from the controls. The split-sample analysis produced a similar pattern but with a lower accuracy for group discrimination in both the derivation (80%) and the validation (73%) samples. Our results showed that resting-state functional MRI can be potentially useful for identification of Parkinson's disease-related spatial covariance patterns, and for differentiation of Parkinson's disease patients from healthy controls at an individual level.Journal of Cerebral Blood Flow & Metabolism advance online publication, 3 June 2015; doi:10.1038/jcbfm.2015.118.
Journal of cerebral blood flow and metabolism: official journal of the International Society of Cerebral Blood Flow and Metabolism 06/2015; DOI:10.1038/jcbfm.2015.118 · 5.41 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Objective We investigated whether systemic lupus erythematosus (SLE) disease duration or serology associate with abnormal regional glucose metabolism as measured with [18F]2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) and deficits on neuropsychological testing.
Methods Subjects with SLE with stable disease activity, without brain damage or clinical symptoms of neuropsychiatric (NP) SLE, stratified by disease duration (short-term (ST)-SLE=disease ≤2 years, long-term (LT)-SLE=disease ≥10 years), underwent clinical assessments, neuropsychological testing, resting FDG-PET scan imaging and measurement of serum titres of antibody to N-methyl-d-aspartate receptor (DNRAb). FDG-PET scans were compared with age-matched and gender-matched healthy controls.
Results Subjects with LT-SLE demonstrated hypometabolism in the prefrontal and premotor cortices that correlated with accrued SLE-related damage, but not with DNRAb titre or performance on NP testing. Independent of disease duration, subjects with SLE demonstrated hypermetabolism in the hippocampus and orbitofrontal cortex that correlated with impaired memory performance and mood alterations (depression, anxiety, fatigue). Serum DNRAb also correlated independently with impaired memory performance and increased anxiety. Together, serum DNRAb titre and regional hypermetabolism were more powerful predictors of performance than either alone.
Interpretation The presence of serum DNRAbs can account for some aspects of brain dysfunction in patients with SLE, and the addition of regional measurements of resting brain metabolism improves the assessment and precise attribution of central nervous system manifestations related to SLE.
[Show abstract][Hide abstract] ABSTRACT: 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.
Proceedings of the National Academy of Sciences 02/2015; 112(8). DOI:10.1073/pnas.1411011112 · 9.67 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Rapid eye movement sleep behaviour disorder has been evaluated using Parkinson's disease-related metabolic network. It is unknown whether this disorder is itself associated with a unique metabolic network. (18)F-fluorodeoxyglucose positron emission tomography was performed in 21 patients (age 65.0 ± 5.6 years) with idiopathic rapid eye movement sleep behaviour disorder and 21 age/gender-matched healthy control subjects (age 62.5 ± 7.5 years) to identify a disease-related pattern and examine its evolution in 21 hemi-parkinsonian patients (age 62.6 ± 5.0 years) and 16 moderate parkinsonian patients (age 56.9 ± 12.2 years). We identified a rapid eye movement sleep behaviour disorder-related metabolic network characterized by increased activity in pons, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, and posterior cerebellum, with decreased activity in occipital and superior temporal regions. Compared to the healthy control subjects, network expressions were elevated (P < 0.0001) in the patients with this disorder and in the parkinsonian cohorts but decreased with disease progression. Parkinson's disease-related network activity was also elevated (P < 0.0001) in the patients with rapid eye movement sleep behaviour disorder but lower than in the hemi-parkinsonian cohort. Abnormal metabolic networks may provide markers of idiopathic rapid eye movement sleep behaviour disorder to identify those at higher risk to develop neurodegenerative parkinsonism.
[Show abstract][Hide abstract] ABSTRACT: Neuroimaging of cerebral glucose metabolism and blood flow is ideally suited to assay widely-distributed brain circuits as a result of local molecular events and behavioral modulation in the central nervous system. With the progress in novel analytical methodology, this endeavor has succeeded in unraveling the mechanisms underlying a wide spectrum of neurodegenerative diseases. In particular, statistical brain mapping studies have made significant strides in describing the pathophysiology of Parkinson's disease (PD) and related disorders by providing signature biomarkers to determine the systemic abnormalities in brain function and evaluate disease progression, therapeutic responses, and clinical correlates in patients. In this article, we review the relevant clinical applications in patients in relation to healthy volunteers with a focus on the generation of unique spatial covariance patterns associated with the motor and cognitive symptoms underlying PD. These characteristic biomarkers can be potentially used not only to improve patient recruitment but also to predict outcomes in clinical trials.
[Show abstract][Hide abstract] ABSTRACT: 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.
[Show abstract][Hide abstract] ABSTRACT: Patient responses to placebo and sham effects are a major obstacle to the development of therapies for brain disorders, including Parkinson's disease (PD). Here, we used functional brain imaging and network analysis to study the circuitry underlying placebo effects in PD subjects randomized to sham surgery as part of a double-blind gene therapy trial. Metabolic imaging was performed prior to randomization, then again at 6 and 12 months after sham surgery. In this cohort, the sham response was associated with the expression of a distinct cerebello-limbic circuit. The expression of this network increased consistently in patients blinded to treatment and correlated with independent clinical ratings. Once patients were unblinded, network expression declined toward baseline levels. Analogous network alterations were not seen with open-label levodopa treatment or during disease progression. Furthermore, sham outcomes in blinded patients correlated with baseline network expression, suggesting the potential use of this quantitative measure to identify "sham-susceptible" subjects before randomization. Indeed, Monte Carlo simulations revealed that a priori exclusion of such individuals substantially lowers the number of randomized participants needed to demonstrate treatment efficacy. Individualized subject selection based on a predetermined network criterion may therefore limit the need for sham interventions in future clinical trials.
[Show abstract][Hide abstract] ABSTRACT: Our previous dosimetry studies have demonstrated that for dopaminergic radiotracers, (18)F-FDOPA and (18)F-FPCIT, the urinary bladder is the critical organ. As these tracers accumulate in the basal ganglia (BG) with high affinity and long residence times, radiation dose to the BG may become significant, especially in normal control subjects. We have performed dynamic PET measurements using (18)F-FPCIT in 16 normal adult subjects to determine if in fact the BG, although not a whole organ, but a well-defined substructure, receives the highest dose. Regions of interest were drawn over left and right BG structures. Resultant time-activity curves were generated and used to determine residence times for dosimetry calculations. S-factors were computed using the MIRDOSE3 nodule model for each caudate and putamen. For (18)F-FPCIT, BG dose ranged from 0.029 to 0.069 mGy/MBq. In half of all subjects, BG dose exceeded 85% of the published critical organ (bladder) dose, and in three of those, the BG dose exceeded that for the bladder. The BG can become the dose-limiting organ in studies using dopamine transporter ligands. For some normal subjects studied with F-18 or long half-life radionuclide, the BG may exceed bladder dose and become the critical structure.
BioMed Research International 06/2014; 2014:498072. DOI:10.1155/2014/498072 · 1.58 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: There is a compelling need for early, accurate diagnosis of Parkinson's disease (PD). Various magnetic resonance imaging modalities are being explored as an adjunct to diagnosis. A significant challenge in using MR imaging for diagnosis is developing appropriate algorithms for extracting diagnostically relevant information from brain images. In previous work, we have demonstrated that individual subject variability can have a substantial effect on identifying and determining the borders of regions of analysis, and that this variability may impact on prediction accuracy. In this paper we evaluate a new statistical algorithm to determine if we can improve accuracy of prediction using a subjects left-out validation of a DTI analysis. Twenty subjects with PD and 22 healthy controls were imaged to evaluate if a full brain diffusion tensor imaging-fractional anisotropy (DTI-FA) map might be capable of segregating PD from controls. In this paper, we present a new statistical algorithm based on bootstrapping. We compare the capacity of this algorithm to classify the identity of subjects left out of the analysis with the accuracy of other statistical techniques, including standard cluster-thresholding. The bootstrapped analysis approach was able to correctly discriminate the 20 subjects with PD from the 22 healthy controls (area under the receiver operator curve or AUROC 0.90); however the sensitivity and specificity of standard cluster-thresholding techniques at various voxel-specific thresholds were less effective (AUROC 0.72-0.75). Based on these results sufficient information to generate diagnostically relevant statistical maps may already be collected by current MRI scanners. We present one statistical technique that might be used to extract diagnostically relevant information from a full brain analysis.
[Show abstract][Hide abstract] ABSTRACT: Dystonia is a brain disorder characterized by abnormal involuntary movements without defining neuropathological changes. The disease is often inherited as an autosomal-dominant trait with incomplete penetrance. Individuals with dystonia, whether inherited or sporadic, exhibit striking phenotypic variability, with marked differences in the somatic distribution and severity of clinical manifestations. In the current study, we used magnetic resonance diffusion tensor imaging to identify microstructural changes associated with specific limb manifestations. Functional MRI was used to localize specific limb regions within the somatosensory cortex. Microstructural integrity was preserved when assessed in subrolandic white matter regions somatotopically related to the clinically involved limbs, but was reduced in regions linked to clinically uninvolved (asymptomatic) body areas. Clinical manifestations were greatest in subjects with relatively intact microstructure in somatotopically relevant white matter regions. Tractography revealed significant phenotype-related differences in the visualized thalamocortical tracts while corticostriatal and corticospinal pathways did not differ between groups. Cerebellothalamic microstructural abnormalities were also seen in the dystonia subjects, but these changes were associated with genotype, rather than with phenotypic variation. The findings suggest that the thalamocortical motor system is a major determinant of dystonia phenotype. This pathway may represent a novel therapeutic target for individuals with refractory limb dystonia.