David Eidelberg

The Feinstein Institute for Medical Research, New York City, New York, United States

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Publications (291)1601.59 Total impact

  • Shichun Peng, David Eidelberg, Yilong Ma
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    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.
    Neuroscience Bulletin 10/2014; 30(5):823-37. · 1.37 Impact Factor
  • Ji Hyun Ko, Renata P. Lerner, David Eidelberg
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    ABSTRACT: Levodopa (l-dopa) has been at the forefront of antiparkinsonian therapy for a half century. Recent advances in functional brain imaging have contributed substantially to the understanding of the effects of l-dopa and other dopaminergic treatment on the activity of abnormal motor and cognitive brain circuits in Parkinson's disease patients. Progress has also been made in understanding the functional pathology of dyskinesias, a common side effect of l-dopa treatment, at both regional and network levels. Here, we review these studies, focusing mainly on the new mechanistic insights provided by metabolic brain imaging and network analysis. © 2014 International Parkinson and Movement Disorder Society
    Movement Disorders 10/2014; · 5.63 Impact Factor
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    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.
    Brain : a journal of neurology. 09/2014;
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    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.
    Journal of Clinical Investigation 07/2014; 124(8):3656-3666. · 12.81 Impact Factor
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    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.
    Neuroinformatics 06/2014; · 3.14 Impact Factor
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    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.
    Cerebral cortex (New York, N.Y. : 1991). 05/2014;
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    ABSTRACT: Systemic lupus erythematosus (SLE) is characterized by multiorgan inflammation, neuropsychiatric disorders (NPSLE), and anti-nuclear antibodies. We previously identified a subset of anti-DNA antibodies (DNRAb) cross-reactive with the N-methyl-D-aspartate receptor, present in 30% to 40% of patients, able to enhance excitatory post-synaptic potentials and trigger neuronal apoptosis. DNRAb+ mice exhibit memory impairment or altered fear response, depending on whether the antibody penetrates the hippocampus or amygdala. Here, we used 18F-fluorodeoxyglucose (FDG) microPET to plot changes in brain metabolism after regional blood-brain barrier (BBB) breach. In DNRAb+ mice, metabolism declined at the site of BBB breach in the first 2 weeks and increased over the next 2 weeks. In contrast, DNRAb- mice exhibited metabolic increases in these regions over the 4 weeks after the insult. Memory impairment was present in DNRAb+ animals with hippocampal BBB breach and altered fear conditioning in DNRAb+ mice with amygdala BBB breach. In DNRAb+ mice, we observed an inverse relationship between neuron number and regional metabolism, while a positive correlation was observed in DNRAb- mice. These findings suggest that local metabolic alterations in this model take place through different mechanisms with distinct time courses, with important implications for the interpretation of imaging data in SLE subjects.Journal of Cerebral Blood Flow & Metabolism advance online publication, 14 May 2014; doi:10.1038/jcbfm.2014.85.
    Journal of cerebral blood flow and metabolism: official journal of the International Society of Cerebral Blood Flow and Metabolism 05/2014; · 5.46 Impact Factor
  • Florian Holtbernd, David Eidelberg
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    ABSTRACT: The differential diagnosis of parkinsonian syndromes can be challenging, particularly in early disease stages. However, prognosis and therapeutic regimes are not alike in Parkinson disease and atypical parkinsonism, and thus a correct diagnosis at the earliest possible stage is desirable. Over the past two decades, magnetic resonance imaging and radiotracer-based imaging techniques have proven to be helpful tools to enhance the accuracy of clinical diagnosis in these disorders. Here, we review recent advances in neuroimaging for the differential diagnosis of parkinsonian syndromes.
    Seminars in neurology. 04/2014; 34(2):202-209.
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    ABSTRACT: 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. 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 (18)F-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. 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 (r(2) = 0.64, p < 0.0001). Latent network abnormalities in subjects with idiopathic RBD are associated with a greater likelihood of subsequent phenoconversion to a progressive neurodegenerative syndrome.
    Neurology 01/2014; · 8.25 Impact Factor
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    ABSTRACT: Evidence exists for late-life depression (LLD) as both a prodrome of and risk factor for Alzheimer’s disease (AD). The underlying neurobiological mechanisms are poorly understood. Impaired peripheral glucose metabolism may explain the association between depression and AD given the connection between type 2 diabetes mellitus with both depression and AD. Positron emission tomography (PET) measures of cerebral glucose metabolism are sensitive to detecting changes in neural circuitry in LLD and AD. Fasting serum glucose (FSG) in non-diabetic young (YC; n=20) and elderly controls (EC; n=12) and LLD patients (n=16) was correlated with PET scans of cerebral glucose metabolism on a voxel-wise basis. The negative correlations were more extensive in EC versus YC and in LLD patients versus EC. Increased FSG correlated with decreased cerebral glucose metabolism in LLD patients to a greater extent than in EC in heteromodal association cortices involved in mood symptoms and cognitive deficits observed in LLD and dementia. Negative correlations in YC were observed in sensory and motor regions. Understanding the neurobiological consequences of diabetes and associated conditions will have substantial public health significance given that this is a modifiable risk factor for which prevention strategies could have an important impact on lowering dementia risk.
    Psychiatry Research Neuroimaging 01/2014; · 3.36 Impact Factor
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    ABSTRACT: Multivariate analytical routines have become increasingly popular in the study of cerebral function in health and in disease states. Spatial covariance analysis of functional neuroimaging data has been used to identify and validate characteristic topographies associated with specific brain disorders. Voxel-wise correlations can be used to assess similarities and differences that exist between covariance topographies. While the magnitude of the resulting topographical correlations is critical, statistical significance can be difficult to determine in the setting of large data vectors (comprised of over 100,000 voxel weights) and substantial autocorrelation effects. Here, we propose a novel method to determine the p-value of such correlations using pseudo-random network simulations.
    PLoS ONE 01/2014; 9(1):e88119. · 3.53 Impact Factor
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    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 01/2014; 2014:498072. · 2.71 Impact Factor
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    ABSTRACT: The dystonias comprise a group of syndromes characterized by prolonged involuntary muscle contractions resulting in repetitive movements and abnormal postures. Primary dystonia has been associated with over 14 different genotypes, most of which follow an autosomal dominant inheritance pattern with reduced penetrance. Independent of etiology, the disease is characterized by extensive variability in disease phenotype and clinical severity. Recent neuroimaging studies investigating this phenomenon in manifesting and non-manifesting genetic carriers of dystonia have discovered microstructural integrity differences in the cerebello-thalamo-cortical tract in both groups related to disease penetrance. Further study suggests these differences to be specific to subrolandic white matter regions somatotopically related to clinical phenotype. Clinical severity was correlated to the degree of microstructural change. These findings suggest a mechanism for the penetrance and clinical variability observed in dystonia and may represent a novel therapeutic target for patients with refractory limb symptoms.
    Current Neurology and Neuroscience Reports 11/2013; 13(11):401. · 3.78 Impact Factor
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    ABSTRACT: 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 Huntington's 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.
    The Journal of clinical investigation 08/2013; · 15.39 Impact Factor
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    ABSTRACT: To generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [(18) F]fluorodeoxyglucose PET scans from patients with PD and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5, and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in patients with PD imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.
    Human Brain Mapping 05/2013; · 6.88 Impact Factor
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    ABSTRACT: Prior evidence has suggested a link between caudate dopaminergic functioning and cognition in Parkinson's disease (PD). In this dual tracer study we analyzed the relationship between nigrostriatal dopaminergic dysfunction and the expression of the previously validated PD cognition-related metabolic pattern (PDCP). In this study, 17 non-demented PD patients underwent positron emission tomography (PET) imaging with [(18)F]-fluorodeoxyglucose to measure PDCP expression, and [(18)F]-fluoropropyl-β-CIT (FPCIT) to measure dopamine transporter (DAT) binding. Automated voxel-by-voxel searches of the FPCIT PET volumes were performed to identify regions in which DAT binding significantly correlated with PDCP expression values. The findings were validated using prespecified anatomical regions-of-interest (ROIs). Voxel-wise interrogation of the FPCIT PET scans revealed a single significant cluster in which DAT binding correlated with PDCP expression (p<0.05, corrected). This cluster was localized to the left caudate nucleus; an analogous correlation (r=-0.63, p<0.01) was also present in the "mirror" region of the right hemisphere. These findings were confirmed by the presence of a significant correlation (r=-0.67, p<0.005) between PDCP expression and DAT binding in caudate ROIs, which survived adjustment for age, disease duration, and clinical severity ratings. Correlation between caudate DAT binding and subject expression of the PD motor-related metabolic pattern was not significant (p>0.21). In summary, this study demonstrates a significant relationship between loss of dopaminergic input to the caudate nucleus and the expression of a cognition-related disease network in unmedicated PD patients. These baseline measures likely function in concert to determine the cognitive effects of dopaminergic therapy in PD.
    NeuroImage 04/2013; · 6.25 Impact Factor
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    ABSTRACT: We used a network approach to assess systems-level abnormalities in motor activation in humans with Parkinson's 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 O-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.
    Journal of Neuroscience 03/2013; 33(10):4540-9. · 6.91 Impact Factor
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    ABSTRACT: The therapeutic benefits of bilateral capsulotomy for the treatment of refractory obsessive compulsive disorder (OCD) are probably attributed to interruption of the cortico-striato-thalamo-cortical circuitry. We evaluated resting brain metabolism and treatment response in OCD patients using positron emission tomography (PET) imaging. [(18)F]-fluoro-deoxy-glucose PET was performed in eight OCD patients precapsulotomy and postcapsulotomy. We determined metabolic differences between preoperative images in patients and those in eight age-matched healthy volunteers, and postoperative changes and clinical correlations in the patients. The OCD patients showed widespread metabolic increases in normalized glucose metabolism in the bilateral orbitofrontal cortex and inferior frontal gyrus, cingulate gyrus, and bilateral pons/cerebellum, and metabolic decreases bilaterally in the precentral and lingual gyri. Bilateral capsulotomy resulted in significant metabolic decreases bilaterally in the prefrontal cortical regions, especially in the dorsal anterior cingulate cortex (ACC) and in the medial dorsal thalamus and caudate nucleus. In contrast, metabolism increased bilaterally in the precentral and lingual gyri. Clinical improvement in patients correlated with metabolic changes in the bilateral dorsal ACC and in the right middle occipital gyrus after capsulotomy. This study underscores the importance of the internal capsule in modulating ventral prefrontal and dorsal anterior cingulate neuronal activity in the neurosurgical management of OCD patients.Journal of Cerebral Blood Flow & Metabolism advance online publication, 27 February 2013; doi:10.1038/jcbfm.2013.23.
    Journal of cerebral blood flow and metabolism: official journal of the International Society of Cerebral Blood Flow and Metabolism 02/2013; · 5.46 Impact Factor
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    ABSTRACT: OBJECTIVE: To identify brain regions with metabolic changes in DYT11 myoclonus-dystonia (DYT11-MD) relative to control subjects and to compare metabolic abnormalities in DYT11-MD with those found in other forms of hereditary dystonia and in posthypoxic myoclonus. METHODS: [(18)F]-fluorodeoxyglucose PET was performed in 6 subjects with DYT11-MD (age 30.5 ± 10.1 years) and in 6 nonmanifesting DYT11 mutation carriers (NM-DYT11; age 59.1 ± 8.9 years) representing the parental generation of the affected individuals. These data were compared to scan data from age-matched healthy control subjects using voxel-based whole brain searches and group differences were considered significant at p < 0.05 (corrected, statistical parametric mapping). As a secondary analysis, overlapping abnormalities were identified by comparisons to hereditary dystonias (DYT1, DYT6, dopa-responsive dystonia) and to posthypoxic myoclonus. RESULTS: We found significant DYT11 genotype-specific metabolic increases in the inferior pons and in the posterior thalamus as well as reductions in the ventromedial prefrontal cortex. Significant phenotype-related increases were present in the parasagittal cerebellum. This latter abnormality was shared with posthypoxic myoclonus, but not with other forms of dystonia. By contrast, all dystonia cohorts exhibited significant metabolic increases in the superior parietal lobule. CONCLUSIONS: The findings are consistent with a subcortical myoclonus generator in DYT11-MD, likely involving the cerebellum. By contrast, subtle increases in the superior parietal cortex relate to the additional presence of dystonic symptoms. Although reduced penetrance in DYT11-MD has been attributed to the maternal imprinting epsilon-sarcoglycan mutations, NM-DYT11 carriers showed significant metabolic abnormalities that are not explained by this genetic model.
    Neurology 01/2013; · 8.25 Impact Factor

Publication Stats

9k Citations
1,601.59 Total Impact Points

Institutions

  • 2006–2014
    • The Feinstein Institute for Medical Research
      • Center for Neuroscience
      New York City, New York, United States
  • 2013
    • Beth Israel Medical Center
      • Alan and Barbara Mirken Department of Neurology
      New York City, New York, United States
  • 2012
    • Nathan Kline Institute
      Orangeburg, New York, United States
  • 2011–2012
    • Johns Hopkins University
      • Department of Psychiatry and Behavioral Sciences
      Baltimore, MD, United States
    • Stanford Medicine
      • Department of Neurology and Neurological Sciences
      Stanford, California, United States
    • University of British Columbia - Vancouver
      • Pacific Parkinson's Research Centre
      Vancouver, British Columbia, Canada
  • 2010
    • Baycrest
      Toronto, Ontario, Canada
    • Stanford University
      • Department of Neurology and Neurological Sciences
      Stanford, CA, United States
  • 2000–2009
    • North Shore-Long Island Jewish Health System
      • • Department of Neurosurgery
      • • Center for Neurosciences
      New York City, New York, United States
    • New York University
      • Department of Neurology
      New York City, NY, United States
    • Gracie Square Hospital, New York, NY
      New York City, New York, United States
  • 2004–2008
    • Otto-von-Guericke-Universität Magdeburg
      Magdeburg, Saxony-Anhalt, Germany
    • North Shore Medical Center
      Miami, Florida, United States
  • 2007
    • Wisconsin National Primate Research Center
      Madison, Wisconsin, United States
  • 2001–2006
    • North Shore-LIJ Health System
      Manhasset, New York, United States
    • National Taiwan University Hospital
      • Department of Neurology
      Taipei, Taipei, Taiwan
  • 1995–2004
    • New York Presbyterian Hospital
      New York City, New York, United States
  • 1991–2001
    • Cornell University
      • • Department of Psychiatry
      • • Department of Medicine
      Ithaca, NY, United States
  • 1990–2001
    • University of Colorado
      • • Division of Clinical Pharmacology and Toxicology
      • • Department of Medicine
      Denver, CO, United States
  • 1999
    • Devry College of New York, USA
      New York City, New York, United States
    • Max Planck Institute of Psychiatry
      München, Bavaria, Germany
  • 1998
    • NYU Langone Medical Center
      • Department of Neurology
      New York City, NY, United States
  • 1996–1997
    • New York State Psychiatric Institute
      New York City, New York, United States
    • The Ohio State University
      • Department of Neurology
      Columbus, OH, United States
    • University of California, Irvine
      • Department of Neurological Surgery
      Irvine, CA, United States
  • 1994–1997
    • Columbia University
      • Department of Neurology
      New York City, NY, United States
  • 1993
    • Université Libre de Bruxelles
      • Department of Nephrology and Urology
      Brussels, BRU, Belgium
  • 1990–1991
    • Memorial Sloan-Kettering Cancer Center
      • Department of Neurology
      New York City, NY, United States