We detected and mapped a dynamically spreading wave of gray matter loss in the brains of patients with Alzheimer's disease (AD). The loss pattern was visualized in four dimensions as it spread over time from temporal and limbic cortices into frontal and occipital brain regions, sparing sensorimotor cortices. The shifting deficits were asymmetric (left hemisphere > right hemisphere) and correlated with progressively declining cognitive status (p < 0.0006). Novel brain mapping methods allowed us to visualize dynamic patterns of atrophy in 52 high-resolution magnetic resonance image scans of 12 patients with AD (age 68.4 +/- 1.9 years) and 14 elderly matched controls (age 71.4 +/- 0.9 years) scanned longitudinally (two scans; interscan interval 2.1 +/- 0.4 years). A cortical pattern matching technique encoded changes in brain shape and tissue distribution across subjects and time. Cortical atrophy occurred in a well defined sequence as the disease progressed, mirroring the sequence of neurofibrillary tangle accumulation observed in cross sections at autopsy. Advancing deficits were visualized as dynamic maps that change over time. Frontal regions, spared early in the disease, showed pervasive deficits later (>15% loss). The maps distinguished different phases of AD and differentiated AD from normal aging. Local gray matter loss rates (5.3 +/- 2.3% per year in AD v 0.9 +/- 0.9% per year in controls) were faster in the left hemisphere (p < 0.029) than the right. Transient barriers to disease progression appeared at limbic/frontal boundaries. This degenerative sequence, observed in vivo as it developed, provides the first quantitative, dynamic visualization of cortical atrophic rates in normal elderly populations and in those with dementia.

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This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhoodand adult-onset schizophrenia, bipolar disorder, attention-deficit/ hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages. D 2004 Published by Elsevier Inc.

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Spatial encoding in MR techniques is achieved by sampling the signal as a function of time in the presence of a magnetic field gradient. The gradients are assumed to generate a linear magnetic field gradient, and typical image reconstruction relies upon this approximation. However, high-speed gradients in the current generation of MRI scanners often sacrifice linearity for improvements in speed. Such nonlinearity results in distorted images. The problem is presented in terms of first principles, and a correction method based on a gradient field spherical harmonic expansion is proposed. In our case, the amount of distortion measured within a typical field of view (FOV) required for head imaging is sufficiently large that without the use of some distortion correction technique, the images would be of The quality of an MR image is dependent upon the accuracy with which the physical position is spatially encoded. Since MRI data are now used routinely for stereotaxy, longitudinal studies of atrophy, and functional studies, it is critical to ensure that images have no distortion or inhomogeneity. The principal machine-dependent sources of this inhomogeneity are eddy currents, gradient nonlinearity, and B 0 and B 1 inhomogeneity (14). In this study we present an analytical approach to calculating and removing the effects of nonlinear gradients only. We chose to focus on a gradient-only solution because of the recent interest in short-bore, high-speed gradients. Although peripheral nerve stimulation is a limiting feature of short rise times, such gradients have been found to be useful in high-speed echo-planar imaging (EPI) of the heart and diffusion tensor imaging (DTI) of the brain. To achieve short rise times and avoid peripheral nerve stimulation, designers have restricted the length of gradients and limited the number of turns in those gradients. Although these constraints are suitable for the implementation of pulse sequences with the desired speed, they have the undesired consequence of increased nonlinearity.Nonlinear pulsed field gradients induce image distortions due to incorrect spatial encoding of the signal. If we assume that the field gradients are linear, it follows that k-space is sampled linearly, and thus the fast Fourier transform (FFT) is suitable for reconstruction. However, any deviation from linearity in the gradients results in nonlinear data sampling and subsequent errors in image spatial encoding. A nonlinear FT would allow these data to be correctly transformed to an image. Unfortunately, nonlinear FT greatly increases computation time by N/log 2 N, relative to an FFT, which makes real-time image generation computationally prohibitive. Here we present a general analytical solution to correct image distortions induced by gradient nonlinearity. The method is applicable to any gradient configuration. It is robust and, more importantly, is based upon an approach whereby the FFT is maintained for image reconstruction.The notion of mapping and correcting such MRI distortions is not new. Two early pa...

Background: Magnetic resonance diffusion tensor imaging (DTI) shows promise in the early detection of microstructural pathophysiological changes in the brain. Objectives: To measure microstructural differences in the brains of participants with amnestic mild cognitive impairment (MCI) compared with an age-matched control group using an optimised DTI technique with fully automated image analysis tools and to investigate the correlation between diffusivity measurements and neuropsychological performance scores across groups. Methods: 34 participants (17 participants with MCI, 17 healthy elderly adults) underwent magnetic resonance imaging (MRI)-based DTI. To control for the effects of anatomical variation, diffusion images of all participants were registered to standard anatomical space. Significant statistical differences in diffusivity measurements between the two groups were determined on a pixel-by-pixel basis using gaussian random field theory. Results: Significantly raised mean diffusivity measurements (p,0.001) were observed in the left and right entorhinal cortices (BA28), posterior occipital-parietal cortex (BA18 and BA19), right parietal supramarginal gyrus (BA40) and right frontal precentral gyri (BA4 and BA6) in participants with MCI. With respect to fractional anisotropy, participants with MCI had significantly reduced measurements (p,0.001) in the limbic parahippocampal subgyral white matter, right thalamus and left posterior cingulate. Pearson's correlation coefficients calculated across all participants showed significant correlations between neuropsychological assessment scores and regional measurements of mean diffusivity and fractional anisotropy. Conclusions: DTI-based diffusivity measures may offer a sensitive method of detecting subtle microstructural brain changes associated with preclinical Alzheimer's disease.