A major goal in Alzheimer's disease (AD) research is to develop biomarkers that are sensitive to early disease, predict decline in those with mild symptoms (e.g. Mild Cognitive Impairment, or MCI), and reflect disease progression. Over the last two decades, a number of candidate neuroimaging, molecular,and psychometric measures have demonstrated variable success in accomplishing these goals. While significant advances have been made with molecular markers (e.g. CSF A?1-42) that are sensitive to specific pathology, these techniques appear relatively insensitive to clinical status or disease progression. On both empirical and theoretical grounds, brain measures that reflect synaptic function are thought to be the most sensitive to the consequences of early AD pathology and predictive of future decline. Fluorodeoyglucose (FDG) PET, a measure of glucose metabolism (CMRGlu), has demonstrated considerable promise in this regard. Arterial spin labeling (ASL) MRI, which is sensitive to cerebral blood flow (CBF) reflective of metabolic activity, may provide overlapping information with FDG-PET, but has several potential advantages: 1) ASL can be acquired in several minutes during routine MR imaging that most patients will obtain as part of their clinical evaluation, and, thus, is less expensive and burdensome~ 2) ASL does not require IV contrast or radiation exposure~ 3) ASL is potentially more accessible than PET~ 4) Short activation or task-related sequences can more easily be implemented with potential for increased sensitivity to early functional change. Further, since ASL is acquired along with other MRI sequences, one can potentially take advantage of orthogonal measures of brain structure and function, the combination of which may offer the fullest characterization of disease state. The central goal of this proposal is to demonstrate that 'state-of-the art'ASL-MRI produces largely equivalent information to FDG-PET in a cohort of amnestic MCI patients. In particular, we will determine the relative capacity of these modalities to determine clinical status [MCI vs healthy control (HC)], disease state (presence/absence of AD CSF profile), and predict future progression. 'Optimized'ASL sequences, leveraging numerous advancements in data acquisition and analysis, will also be compared to a commercially available ASL measure being implemented in the Alzheimer's disease Neuroimaging Initiative renewal (ADNI 2), to determine the relative value of these ASL variants. Additionally, task-related ASL will be explored for its potential to further enhance the predictive value of rest ASL alone. To achieve these aims, MCI patients and HC will undergo a baseline ASL-MRI and FDG-PET scan~ we will also obtain CSF molecular markers (tau/A?). Longitudinal clinical follow-up and a 1-year repeat MRI will allow for assessment of disease progression and determination of the relative predictive value of these imaging biomarkers. Finally, we will utilize the analytic pipeline developed in this project o analyze ASL data from ADNI 2, which will potentially enhance power to address some of the above questions and serve as a replication dataset.

Public Health Relevance

The development of biomarkers that are sensitive to early Alzheimer's disease (AD), predict rate of decline, and track disease progression is a major focus of AD research. Inexpensive and non-invasive MRI biomarkers are of great potential utility in clinical trials of putative disease modifying interventions, potentially reducing sample sizes and length of trials. These biomarkers will also likely play an important role in clinical practice for screening, prognosis and, with the emergence of disease modifying therapeutic options, disease monitoring. Given our aging population and estimates of future AD prevalence, development of these biomarkers is likely to have significant public health impact.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG040271-03
Application #
8663811
Study Section
Clinical Neuroscience and Neurodegeneration Study Section (CNN)
Program Officer
Hsiao, John
Project Start
2012-08-01
Project End
2017-05-31
Budget Start
2014-07-01
Budget End
2015-05-31
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Neurology
Type
Schools of Medicine
DUNS #
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Xie, Long; Das, Sandhitsu R; Wisse, Laura E M et al. (2018) Early Tau Burden Correlates with Higher Rate of Atrophy in Transentorhinal Cortex. J Alzheimers Dis 62:85-92
Vidorreta, Marta; Wang, Ze; Chang, Yulin V et al. (2017) Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals readout. PLoS One 12:e0183762
Xie, Long; Pluta, John B; Das, Sandhitsu R et al. (2017) Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability. Neuroimage 144:183-202
Wisse, L E M; Adler, D H; Ittyerah, R et al. (2017) Comparison of In Vivo and Ex Vivo MRI of the Human Hippocampal Formation in the Same Subjects. Cereb Cortex 27:5185-5196
Dolui, Sudipto; Vidorreta, Marta; Wang, Ze et al. (2017) Comparison of PASL, PCASL, and background-suppressed 3D PCASL in mild cognitive impairment. Hum Brain Mapp 38:5260-5273
Dolui, Sudipto; Wang, Ze; Shinohara, Russell T et al. (2017) Structural Correlation-based Outlier Rejection (SCORE) algorithm for arterial spin labeling time series. J Magn Reson Imaging 45:1786-1797
Xie, Long; Dolui, Sudipto; Das, Sandhitsu R et al. (2016) A brain stress test: Cerebral perfusion during memory encoding in mild cognitive impairment. Neuroimage Clin 11:388-397
Smith, Kara M; Xie, Sharon X; Weintraub, Daniel (2016) Incident impulse control disorder symptoms and dopamine transporter imaging in Parkinson disease. J Neurol Neurosurg Psychiatry 87:864-70
Xie, Long; Wisse, Laura E M; Das, Sandhitsu R et al. (2016) Accounting for the Confound of Meninges in Segmenting Entorhinal and Perirhinal Cortices in T1-Weighted MRI. Med Image Comput Comput Assist Interv 9901:564-571
Kandel, Benjamin M; Avants, Brian B; Gee, James C et al. (2016) White matter hyperintensities are more highly associated with preclinical Alzheimer's disease than imaging and cognitive markers of neurodegeneration. Alzheimers Dement (Amst) 4:18-27

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