IC‐PL‐04: Future imaging techniques lecture: Diffusion spectrum imaging

SATURDAY, JULY 10, 2010 ALZHEIMER’S IMAGING CONSORTIUM IC-PL AIC PLENARY IC-PL-01 DIAGNOSTIC MARKERS: DETECTION OF PRECLINICAL PATHOLOGY, PROGNOSTIC CAPABILITIES, AND DYNAMIC METABOLISM OF ABETA AND TAU David Holtzman, Washington University, St. Louis, MO, USA. Contact e-mail: holtzman@neuro.wustl.edu Background: The amyloid-beta (Abeta) peptide and tau play key roles in the pathogenesis of Alzheimer’s disease (AD). A large body of data suggests that the aggregation of the normally soluble Abeta peptide and tau into a variety of forms and their build up in the brain are critical events in initiation and progression of AD. Methods: To further determine the order of events in AD pathogenesis and to develop antecedent biomarkers, analysis of CSF Abeta and tau has been performed along with structural and amyloid imaging in the cognitively normal middle-aged and elderly subjects followed longitudinally. Results: CSF and imaging studies suggest that Abeta begins to aggregate in the brain many years prior to the earliest clinically detectable abnormalities associated with dementia. Data suggests that these changes are first detectable by a drop in CSF Abeta42 followed by the detection of fibrillar amyloid in the brain with amyloid imaging. While subjects are still cognitively normal, there is an increase in CSF tau and p-tau in some subjects such that the amount of fibrillar amyloid significantly correlates with CSF tau and p-tau. Amyloid deposition in the preclinical period is associated with smaller brain volume and future volume loss. Increases in tau are associated with smaller brain volume in demented subjects but not in those who are cognitively normal. The ratio of CSF tau/Abeta42 is strongly predictive of who will convert from normal cognition to develop very mild dementia/MCI over a 2-4 year period. Utilizing a new metabolic labeling technique, one can measure the synthesis and clearance of Abeta and other proteins in the human CSF which has revealed the synthesis and clearance rates of CNS Abeta and the effects of treatments targeting Abeta. Conclusions: Data suggests that Abeta aggregation is upstream of tau in AD but that aggregation of both proteins is initiated well before clinical symptoms and signs of AD. Measurements of Abeta42, tau, and other CSF proteins should facilitate selection of subjects for clinical trials in cognitively normal individuals. Dynamic measurement of Abeta and other proteins using new methods is providing new mechanistic insights into AD pathogenesis and treatment. IC-PL-02 TRANSLATING DISCOVERIES FROM MOUSE MODELS TO THE CLINIC: SUCCESSES AND CHALLENGES Frank M. LaFerla, University of California, Irvine, Irvine, CA, USA. Contact e-mail: laferla@uci.edu Background: Over 35 million people throughout the world are currently afflicted with Alzheimer’s disease (AD). Unless the disease course is altered, it is anticipated that the number of AD patients worldwide will soar, approaching 115 million by the year 2050. Hence, there is an urgent need to identify novel pharmaceutical, lifestyle, and dietary factors that can prevent, delay or retard the progression of AD. Animals models represent a key strategy for identifying and evaluating these therapeutic approaches. Although none of the existing models truly mimics all of the disease features, the models are still valuable, and offer the opportunity to elucidate the underlying mechanism by which these therapies work. Methods: We have employed a variety of approaches (genetic, pharmacological, and lifestyle such as diet and cognitive stimulation) to try and reverse the neuropathological lesions and to mitigate the learning and memory deficits in the 3xTg-AD mouse model. Results: Our findings indicate that the timing when treatment is initiated plays a critical role in determining therapeutic efficacy, as some therapies are not effective when administered after extensive pathology has developed. We have also focused efforts on inducing neuronal cell death in brain areas impacted in AD, as reversing cognitive loss is likely to be more challenging following cell loss. Lastly, we have found that some cell-based therapies can reverse the cognitive deficits without lowering Aß or tau pathology. Conclusions: Not all treatments are effective at curing AD in mice. The timing of the initiation of treatment is critical, as some therapies are not effective when administered after extensive pathology has developed. Combination therapies (polypharmacy) will likely be required to successfully treat AD in humans. IC-PL-03 RESTING NETWORKS AND NEURODEGENERATIVE DISORDERS: THE WAY FORWARD Michael D. Greicius, Stanford University Medical Center, Stanford, CA, USA. Contact e-mail: greicius@stanford.edu Background: Resting-state fMRI is a relatively novel approach that is gaining currency in the clinical research domain. This approach uses temporal correlations in spontaneous fMRI signal fluctuations to define functional connectivity between brain regions. Methods: This talk will review some of the basic principles of resting-state fMRI analysis and its potential applications in neurodegenerative disorders. The two basic approaches to detecting functional networks, region-of-interest-based analysis and independent component analysis, will be described. Results: Several disticnt intrinsic connectivity networks will be considered and their relationship to specific neurodegenerative disorders will be explored. Conclusions: The talk will conclude with a discussion of lingering obstacles that most be overcome in order to render this promising technique meaningful at the single-patient level. IC-PL-04 FUTURE IMAGING TECHNIQUES LECTURE: DIFFUSION SPECTRUM IMAGING Van J. Weeden, Massachusetts General Hospital, Charlestown, MA, USA. Contact e-mail: van@nmr.mgh.harvard.edu Abstract not availablenot available SATURDAY, JULY 10, 2010 ALZHEIMER’S IMAGING CONSORTIUM IC-01 ORAL 1 MOLECULAR IMAGING TO UNDERSTAND DISEASE PATHOPHYSIOLOGY IC-01-01 ANTEMORTEM PIB BINDING CORRELATES WITH POSTMORTEM AMYLOID DENSITY IN A CASE WITH DLB ON REGION OF INTEREST ANALYSIS Kejal Kantarci, Chunhui Yang, Julie A. Schneider, Matthew L. Senjem, Denise A. Reyes, Val J. Lowe, Lisa L. Barnes, Neelum T. Aggarwal, David A. Bennett, Glenn E. Smith, David S. Knopman,