The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimer's disease. Magnetic resonance imaging (MRI), (18F)-fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquiredat multiple time points. All data will be cross-linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications thatguided protocol development. A major effort was

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Our results indicate that retrospective techniques have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Approaches using measures of voxel intensity similarity are showing promise in fully automating magnetic resonance (MR) and positron emission tomography (PET) image registration in the head, without requiring extraction and identification of corresponding structures. In this paper a method of multiresolution optimization of these measures is described and five alternative measures are compared: cross correlation, minimization of corresponding PET intensity variation, moments of the distribution of values in the intensity feature space, entropy of the intensity feature space and mutual information. Their ability to recover registration is examined for ten clinically acquired image pairs with respect to the size of initial misregistration, the precision of the final result, and the accuracy assessed by visual inspection. The mutual information measure proved the most robust to initial starting estimate, successfully registering 98.8% of 900 trial misregistrations. Success is defined as providing a visually acceptable solution to a trained observer. A high resolution search (1/16 mm step size) of 30 trial misregistrations showed that optimization using the mutual information measure provided solutions with 0.13 mm, 0.11 mm and 0.17 mm standard deviations in the three Cartesian axes of the translation vector and 0.2 degree, 0.3 degree and 0.2 degree standard deviations for rotations about the three axes. The algorithm takes between 4 and 8 minutes to run on a typical workstation, including visual inspection of the result.

Distributions of proton MR-detected metabolites have been mapped throughout the brain in a group of normal subjects using a volumetric MR spectroscopic imaging (MRSI) acquisition with an interleaved water reference. Data were processed with intensity and spatial normalization to enable voxel-based analysis methods to be applied across a group of subjects. Results demonstrate significant regional, tissue, and genderdependent variations of brain metabolite concentrations, and variations of these distributions with normal aging. The greatest alteration of metabolites with age was observed for whitematter choline and creatine. An example of the utility of the normative metabolic reference information is then demonstrated for analysis of data acquired from a subject who suffered a traumatic brain injury. This study demonstrates the ability to obtain proton spectra from a wide region of the brain and to apply fully automated processing methods. Proton MR spectroscopy (MRS) enables the detection of a number of tissue metabolites that provide sensitive markers of disease or injury, making these techniques of considerable interest for clinical diagnostic purposes and particularly for studies in the brain. The acquisition and analysis of MRS data have several technical challenges that compromise the spatial resolution and accuracy of the resultant metabolite values. Furthermore, metabolic changes with disease and injury can frequently be subtle and diffuse, with the result that metabolite images may not be visually interpretable in the sense of a structural MRI. Therefore, the analysis of MRS data greatly benefits from comparison against a known reference signal.Since in vivo MRS measurements are dependent on the acquisition method used, the reference data must be acquired in an identical manner to that of the data under analysis. It is also necessary to take into account normal variations in metabolite concentrations; for example, variations in metabolite concentrations between tissue type, across different brain regions, and changes with age are well documented (1-14). Other reports have indicated differences in metabolite concentrations with gender, lateralization (15), intelligence quotient (IQ) (16), and associations with smoking and alcohol consumption (17). To account for these factors, results are commonly compared against data obtained from the same location in a group of control subjects matched to the study group under investigation. However, although many studies have reported metabolite values from normal control subjects, these values can rarely be used as the reference information for other investigations. For example, published values using single-voxel spectroscopy (SVS) measurements are limited to only a few brain regions, and acquisition parameters, analysis methods, and subject selection criteria, such as age, vary considerably, making it unlikely that existing data can be used as reference information for a new investigation. Additionally, a sufficient number of measurements must be acquired to accoun...

We propose in this work a patch-based image labeling method relying on a label propagation framework. Based on image intensity similarities between the input image and an anatomy textbook, an original strategy which does not require any non-rigid registration is presented. Following recent developments in non-local image denoising, the similarity between images is represented by a weighted graph computed from an intensity-based distance between patches. Experiments on simulated and in-vivo MR images show that the proposed method is very successful in providing automated human brain labeling.

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Temporal lobe seizures are accompanied by complex behavioral phenomena including loss of consciousness, dystonic movements and neuroendocrine changes. These phenomena may arise from extended neural networks beyond the temporal lobe. To investigate this, we imaged cerebral blood flow (CBF) changes during human temporal lobe seizures with single photon emission computed tomography (SPECT) while performing continuous video/EEG monitoring. We found that temporal lobe seizures associated with loss of consciousness produced CBF increases in the temporal lobe, followed by increases in bilateral midline subcortical structures. These changes were accompanied by marked bilateral CBF decreases in the frontal and parietal association cortex. In contrast, temporal lobe seizures in which consciousness was spared were not accompanied by these widespread CBF changes. The CBF decreases in frontal and parietal association cortex were strongly correlated with increases in midline structures such as the mediodorsal thalamus. These results suggest that impaired consciousness in temporal lobe seizures may result from focal abnormal activity in temporal and subcortical networks linked to widespread impaired function of the association cortex.