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Neuroimaging is a fundamental component of the neurological diagnosis. The greatly increased volume and complexity of neuroimaging datasets has created a need for efficient image management and retrieval. In this paper, we advance a content-based retrieval framework for 3D functional neuroimaging data based on 3D curvelet transforms. The localized volumetric texture feature was extracted by a 3D digital curvelet transform from parametric image of cerebral metabolic rate of glucose consumption with a set of adaptive disorder-oriented masks for each type of neurological disorder. The results, using 142 clinical dementia studies, show that our proposed approach supports efficient and high performance neuroimaging data retrieval.