Publications (179) View all
-
Article: Transcranial sonography and functional imaging in glucocerebrosidase mutation Parkinson disease.
[show abstract] [hide abstract]
ABSTRACT: 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.Parkinsonism & Related Disorders 10/2012; · 3.80 Impact Factor -
Article: Network correlates of disease severity in multiple system atrophy.
[show abstract] [hide abstract]
ABSTRACT: 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. We used [(18)F]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. 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). 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.Neurology 04/2012; 78(16):1237-44. · 8.31 Impact Factor -
Article: Network correlates of the cognitive response to levodopa in Parkinson disease.
[show abstract] [hide abstract]
ABSTRACT: Cognitive dysfunction is common in Parkinson disease (PD), even early in its clinical course. This disease manifestation has been associated with impaired verbal learning performance as well as abnormal expression of a specific PD-related cognitive spatial covariance pattern (PDCP). It is not known, however, how this metabolic network relates to the cognitive response to dopaminergic therapy on the individual patient level. We assessed treatment-mediated changes in verbal learning and PDCP expression in 17 patients with PD without dementia who underwent cognitive testing and metabolic imaging in the unmedicated and levodopa-treated conditions. We also determined whether analogous changes were present in 12 other patients with PD without dementia who were evaluated before and during the treatment of cognitive symptoms with placebo. Levodopa-mediated changes in verbal learning correlated with concurrent changes in PDCP expression (r = -0.60, p < 0.01). The subset of patients with meaningful cognitive improvement on levodopa (n = 8) exhibited concurrent reductions in PDCP expression (p < 0.01) with treatment; network modulation was not evident in the remaining subjects. Notably, the levodopa cognitive response correlated with baseline PDCP levels (r = 0.70, p = 0.002). By contrast, placebo did not affect PDCP expression, even in the subjects (n = 7) with improved verbal learning during treatment. These findings suggest that cognitive dysfunction in PD may respond to treatment depending upon the degree of baseline PDCP expression. Quantification of treatment-mediated network changes can provide objective information concerning the efficacy of new agents directed at the cognitive manifestations of this disease.Neurology 08/2011; 77(9):858-65. · 8.31 Impact Factor -
Article: Abnormal metabolic brain networks in Tourette syndrome.
[show abstract] [hide abstract]
ABSTRACT: To identify metabolic brain networks that are associated with Tourette syndrome (TS) and comorbid obsessive-compulsive disorder (OCD). We utilized [(18)F]-fluorodeoxyglucose and PET imaging to examine brain metabolism in 12 unmedicated patients with TS and 12 age-matched controls. We utilized a spatial covariance analysis to identify 2 disease-related metabolic brain networks, one associated with TS in general (distinguishing TS subjects from controls), and another correlating with OCD severity (within the TS group alone). Analysis of the combined group of patients with TS and healthy subjects revealed an abnormal spatial covariance pattern that completely separated patients from controls (p < 0.0001). This TS-related pattern (TSRP) was characterized by reduced resting metabolic activity of the striatum and orbitofrontal cortex associated with relative increases in premotor cortex and cerebellum. Analysis of the TS cohort alone revealed the presence of a second metabolic pattern that correlated with OCD in these patients. This OCD-related pattern (OCDRP) was characterized by reduced activity of the anterior cingulate and dorsolateral prefrontal cortical regions associated with relative increases in primary motor cortex and precuneus. Subject expression of OCDRP correlated with the severity of this symptom (r = 0.79, p < 0.005). These findings suggest that the different clinical manifestations of TS are associated with the expression of 2 distinct abnormal metabolic brain networks. These, and potentially other disease-related spatial covariance patterns, may prove useful as biomarkers for assessing responses to new therapies for TS and related comorbidities.Neurology 02/2011; 76(11):944-52. · 8.31 Impact Factor -
Conference Proceeding: Three-fold cross-validation of parkinsonian brain patterns
[show abstract] [hide abstract]
ABSTRACT: Abnormal physiological networks of brain areas in disease can be identified by applying specialized multivariate computational algorithms based on principal component analysis to functional image data. Here we demonstrate the reproducibility of network patterns derived using positron emission tomography (PET) data in independent populations of parkinsonian patients for a large, clinically validated data set comprised of subjects with idiopathic Parkinson's disease (iPD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Correlation of voxel values of network patterns derived for the same condition in different data sets was high. To further illustrate the validity of these networks, we performed single subject differential diagnosis of prospective test subjects to determine the most probable case based on a subject's network scores expressed for each of these distinct parkinsonian syndromes. Three-fold cross-validation was performed to determine accuracy and positive predictive rates based on networks derived in separate folds of the composite data set. A logistic regression based classification algorithm was used to train in each fold and test in the remaining two folds. Combined accuracy for each of the three folds ranged from 82% to 93% in the training set and was approximately 81% for prospective test subjects.Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
