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Although reductions in the expression of the calcium-buffering proteins calbindin D-28K (CB) and parvalbumin (PV) have been observed in the aging brain, it is unknown whether these changes contribute to age-related hippocampal dysfunction. To address this issue, we measured basal hippocampal metabolism and hippocampal structure across the lifespan of C57BL/6J, calbindin D-28k knockout (CBKO) and parvalbumin knockout (PVKO) mice. Basal metabolism was estimated using steady state relative cerebral blood volume (rCBV), which is a variant of fMRI that provides the highest spatial resolution, optimal for the analysis of individual subregions of the hippocampal formation. We found that like primates, normal aging in C57BL/6J mice is characterized by an age-dependent decline in rCBV-estimated dentate gyrus metabolism. Although abnormal hippocampal fMRI signals were observed in CBKO and PVKO mice, only CBKO mice showed accelerated age-dependent decline of rCBV-estimated metabolism in the dentate gyrus. We also found age-independent structural changes in CBKO mice, which included an enlarged hippocampus and neocortex as well as global brain hypertrophy. These metabolic and structural changes in CBKO mice correlated with a deficit in hippocampus-dependent learning in the active place avoidance task. Our results suggest that the decrease in CB that occurs during normal aging is involved in age-related hippocampal metabolic decline. Our findings also illustrate the value of using multiple MRI techniques in transgenic mice to investigate mechanisms involved in the functional and structural changes that occur during aging.

In this article, we introduce a dynamic optical tomography system that is, unlike currently available analog instrumentation, based on digital data acquisition and filtering techniques. At the core of this continuous wave instrument is a digital signal processor (DSP) that collects, collates, processes, and filters the digitized data set. The processor is also responsible for managing system timing and the imaging routines which can acquire real-time data at rates as high as 150 Hz. Many of the synchronously timed processes are controlled by a complex programmable logic device that is also used in conjunction with the DSP to orchestrate data flow. The operation of the system is implemented through a comprehensive graphical user interface designed with LABVIEW software which integrates automated calibration, data acquisition, data organization, and signal postprocessing. Performance analysis demonstrates very low system noise (approximately 1 pW rms noise equivalent power), excellent signal precision (<0.04%-0.2%) and long term system stability (<1% over 40 min). A large dynamic range (approximately 190 dB) accommodates a wide scope of measurement geometries and tissue types. First experiments on tissue phantoms show that dynamic behavior is accurately captured and spatial location can be correctly tracked using this system.

Abstract.It is well known that the inverse problem in optical tomography is highly ill-posed. The image reconstruction process is often unstable and nonunique, because the number of the boundary measurements data is far fewer than the number of the unknown parameters to be reconstructed. To overcome this problem, one can either increase the number of measurement data ͑e.g., multispectral or multifrequency methods͒, or reduce the number of unknowns ͑e.g., using prior structural information from other imaging modalities͒. We introduce a novel approach for reducing the unknown parameters in the reconstruction process. The discrete cosine transform ͑DCT͒, which has long been used in image compression, is here employed to parameterize the reconstructed image. In general, only a few DCT coefficients are needed to describe the main features in an optical tomographic image. Thus, the number of unknowns in the image reconstruction process can be drastically reduced. We show numerical and experimental examples that illustrate the performance of the new algorithm as compared to a standard model-based iterative image reconstructions scheme. We especially focus on the influence of initial guesses and noise levels on the reconstruction results.

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