Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by William Jagust
Total Records ( 4 ) for William Jagust
  John Q. Trojanowski , Hugo Vandeerstichele , Magdalena Korecka , Christopher M. Clark , Paul S. Aisen , Ronald C. Petersen , Kaj Blennow , Holly Soares , Adam Simon , Piotr Lewczuk , Robert Dean , Eric Siemers , William Z. Potter , Michael W. Weiner , Clifford R. Jack Jr. , William Jagust , Arthur W. Toga , Virginia M.-Y. Lee and Leslie M. Shaw
  Here, we review progress by the Penn Biomarker Core in the Alzheimer's Disease Neuroimaging Initiative (ADNI) toward developing a pathological cerebrospinal fluid (CSF) and plasma biomarker signature for mild Alzheimer's disease (AD) as well as a biomarker profile that predicts conversion of mild cognitive impairment (MCI) and/or normal control subjects to AD. The Penn Biomarker Core also collaborated with other ADNI Cores to integrate data across ADNI to temporally order changes in clinical measures, imaging data, and chemical biomarkers that serve as mileposts and predictors of the conversion of normal control to MCI as well as MCI to AD, and the progression of AD. Initial CSF studies by the ADNI Biomarker Core revealed a pathological CSF biomarker signature of AD defined by the combination of Aβ1-42 and total tau (T-tau) that effectively delineates mild AD in the large multisite prospective clinical investigation conducted in ADNI. This signature appears to predict conversion from MCI to AD. Data fusion efforts across ADNI Cores generated a model for the temporal ordering of AD biomarkers which suggests that Aβ amyloid biomarkers become abnormal first, followed by changes in neurodegenerative biomarkers (CSF tau, F-18 fluorodeoxyglucose-positron emission tomography, magnetic resonance imaging) with the onset of clinical symptoms. The timing of these changes varies in individual patients due to genetic and environmental factors that increase or decrease an individual's resilience in response to progressive accumulations of AD pathologies. Further studies in ADNI will refine this model and render the biomarkers studied in ADNI more applicable to routine diagnosis and to clinical trials of disease modifying therapies.
  Paul S. Aisen , Ronald C. Petersen , Michael C. Donohue , Anthony Gamst , Rema Raman , Ronald G. Thomas , Sarah Walter , John Q. Trojanowski , Leslie M. Shaw , Laurel A. Beckett , Clifford R. Jack Jr. , William Jagust , Arthur W. Toga , Andrew J. Saykin , John C. Morris , Robert C. Green and Michael W. Weiner
  The Clinical Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has provided clinical, operational, and data management support to ADNI since its inception. This article reviews the activities and accomplishments of the core in support of ADNI aims. These include the enrollment and follow-up of more than 800 subjects in the three original cohorts: healthy controls, amnestic mild cognitive impairment (now referred to as late MCI, or LMCI), and mild Alzheimer’s disease (AD) in the first phase of ADNI (ADNI 1), with baseline longitudinal, clinical, and cognitive assessments. These data, when combined with genetic, neuroimaging, and cerebrospinal fluid measures, have provided important insights into the neurobiology of the AD spectrum. Furthermore, these data have facilitated the development of novel clinical trial designs. ADNI has recently been extended with funding from an NIH Grand Opportunities (GO) award, and the new ADNI GO phase has been launched; this includes the enrollment of a new cohort, called early MCI, with milder episodic memory impairment than the LMCI group. An application for a further 5 years of ADNI funding (ADNI 2) was recently submitted. This funding would support ongoing follow-up of the original ADNI 1 and ADNI GO cohorts, as well as additional recruitment into all categories. The resulting data would provide valuable data on the earliest stages of AD, and support the development of interventions in these critically important populations.
  Beth Kuczynski , Elizabeth Targan , Cindee Madison , Michael Weiner , Yu Zhang , Bruce Reed , Helena C. Chui and William Jagust
  Background Studies show that white matter hyperintensities, regardless of location, primarily affect frontal lobe metabolism and function. This report investigated how regional white matter integrity (measured as fractional anisotropy [FA]) relates to brain metabolism, to unravel the complex relationship between white matter changes and brain metabolism. Objective To elucidate the relationship between white matter integrity and gray matter metabolism using diffusion tensor imaging and fluorodeoxyglucose-positron emission tomography in a cohort of 16 subjects ranging from normal to demented (age, >55 years). Methods Mean FA values from white matter regions underlying the medial prefrontal, inferior-lateral prefrontal, parietal association, and posterior temporal areas and the corpus callosum were regressed with glucose metabolism (by positron emission tomography), using statistical parametric mapping (P < 0.005; voxel cluster, >100). Regional cerebral glucose metabolism was the primary outcome measure. According to our hypothesis, those hypometabolic cortical regions affected by Alzheimer's disease would correlate with a lower FA of associated tracks. Results Our data show inter-regional positive correlations between FA and gray matter metabolism for the prefrontal cortex, temporal, and parietal regions. Our results suggest that left prefrontal FA is associated with left temporal and parietal metabolism. Further, left posterior temporal FA correlated with left prefrontal metabolism. Finally, bilateral parietal FA correlated with bilateral temporal metabolism. Conclusions These regions are associated with cognitive processes affected in Alzheimer's disease and cerebrovascular disease, suggesting a link with white matter degeneration and gray matter hypometabolism. Therefore, cortical function and white matter degeneration are related in aging and dementia.
  Jon B. Toledo , Estefania Toledo , Michael W. Weiner , Clifford R. Jack , William Jagust , Virginia M.-Y. Lee , Leslie M. Shaw and John Q. Trojanowski
  Background There is epidemiological evidence that cardiovascular risk factors (CVRF) also are risk factors for Alzheimer‘s disease, but there is limited information on this from neuropathological studies, and even less from in vivo studies. Therefore, we examined the relationship between CVRF and amyloid-β (Aβ) brain burden measured by Pittsburgh Compound B-positron emission tomography (PiB-PET) studies in the Alzheimer‘s Disease Neuroimaging Initiative. Methods Ninety-nine subjects from the Alzheimer‘s Disease Neuroimaging Initiative cohort who had a PiB-PET study measure, apolipoprotein E genotyping data, and information available on CVRF (body mass index [BMI], systolic blood pressure, diastolic blood pressure [DBP], and cholesterol and fasting glucose test results) were included. Eighty-one subjects also had plasma cortisol, C-reactive protein, and superoxide dismutase 1 measurements. Stepwise regression models were used to assess the relation between the CVRF and the composite PiB-PET score. Results The first model included the following as baseline variables: age, clinical diagnosis, number of apolipoprotein ɛ4 alleles, BMI (P = .023), and DBP (P = .012). BMI showed an inverse relation with PiB-PET score, and DBP had a positive relation with PiB-PET score. In the second adjusted model, cortisol plasma levels were also associated with PiB-PET score (P = .004). Systolic blood pressure, cholesterol, or impaired fasting glucose were not found to be associated with PiB-PET values. Conclusion In this cross-sectional study, we found an association between Aβ brain burden measured in vivo and DBP and cortisol, indicating a possible link between these CVRF and Aβ burden measured by PiB-PET. These findings highlight the utility of biomarkers to explore potential pathways linking diverse Alzheimer‘s disease risk factors.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility