Abstract:
:Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our approach attempts to overcome some shortcomings of the modeling methods used in other neuroimaging settings, by borrowing strength from the spatial correlations present in the data. Our proposed methodology is applicable to data from various imaging modalities including functional magnetic resonance imaging and positron emission tomography, and we provide an illustration here using positron emission tomography data from a study of Alzheimer's disease to predict disease progression.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Derado G,Bowman FD,Zhang L,Alzheimer's Disease Neuroimaging Initiative Investigators.doi
10.1177/0962280212448972subject
Has Abstractpub_date
2013-08-01 00:00:00pages
382-97issue
4eissn
0962-2802issn
1477-0334pii
0962280212448972journal_volume
22pub_type
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