Dynamically Spreading Wave of Gray Matter Loss Visualized in Alzheimer’s Disease using Cortical Pattern Matching

and a Brain Atlas Encoding Atrophic RatesPaul M. Thompson, Kiralee M. Hayashi, Greig de Zubicaray,

Andrew L. Janke, Stephen E. Rose, Stephanie Dittmer, James Semple, David Herman, Michael S. Hong, Michael S. Mega, David M. Doddrell, Arthur W. Toga

Laboratory of Neuro Imaging, UCLA School of Medicine Center for Magnetic Resonance, U. Queensland, Australia

Montreal Neurological Institute, McGill U.,CanadaGlaxoSmithKline Pharmaceuticals plc, Cambridge UK

IntroductionThe Alzheimer’s disease brain atlas integrates maps, models, and a variety of multi-modality images from patients with dementia and controls. New types of mathematical software, applied to the atlas data, can uncover features of the disease process, such as the dynamic pattern of disease progression.

Methods

All the patients are subtracted from controls at their first scan, and then below on their second scan, 3-4 years later. The temporal regions are severely affected, as would be expected from PET and other volumetric MRI studies. The sensorimotor territory, in blue, is comparatively spared. As the disease progresses, an anterior shifting of deficits is shown as the percent deficits intensify and move forward into frontal cortex at the middle stages of the disease, when mean mini-mental score is 18-20 (out of 30). These deficits are also correlated with mini-mental scores in all the affected regions.

ConclusionThe benefit of the statistical atlas comes from its ability to encode data on cortical loss, gyral patterning, and gray matter changes in a probabilistic format. By evaluating cohorts of patients at different stages of Alzheimer’s disease, or different dementia subtypes, or patients undergoing different drug treatments, maps of dynamic features can be made, and therapeutic response can be mapped. The first step in gauging these therapeutic responses effectively is to develop population-based normative criteria and statistics of change for each part of the brain in the form of spatial and temporal maps. This expands the atlas concept to dynamic data, which show enormous promise in clarifying the effects of progressive diseases on the brain, as well as our ability to combat them.

References: Thompson, P.M., Hayashi, K.M., de Zubicaray, G., Janke, A.L., Rose, S.E., Semple, J., Doddrell, D.M., Cannon, T.D., Toga, A.W. (2002). Detecting Dynamic and Genetic Effects on Brain Structure using High-Dimensional Cortical Pattern Matching, Proc. International Symposium on Biomedical Imaging (ISBI2002), Washington, DC, July 7-10, 2002

These loss rate maps can then be convected, using another flow, onto an average hippocampal model in the atlas (see above, figure on left). Here an individual map of volumetric loss is stretched to match an atlas, where it can be compared with loss rate data from other subjects (see above, figure on right).

puts them at increased risk of developing AD. Powerful statistical criteria are needed to see how early brain changes can be detected, when interventions may have maximal effect. To assess drug effects, we also need a computational framework where degenerative rates can be compared across subjects, and relative to normative populations. An AD brain atlas stores statistical data from large populations and allows one to address these questions quantitatively, slowing progressions of the disease in the form of a map.

Sulcal pattern matching is used to create a crisp average model of the cortex from a group of subjects. If the scans are averaged together, and a cortical model is extracted from the average scan, features

Imaging data stored in a disease-specific brain atlas can help assess the severity of a patient’s gray matter loss. This information is interesting for early detection of disease-for example, if the patient has mild cognitive impairment or even in healthy controls with a known genetic variation, such as APO-E4, which

A client-server pipeline, which allows supercomputing algorithms to be run from a desktop machine, processes the data by aligning new scans with a canonical MRI template. For dementia subjects, this is an average brain template that we constructed, with the mean scale and 3D geometry for patients with early dementia.

The aligned data is tissue classified into gray, white and CSF maps and the cortex of each subject is extracted and flattened, so that warping of sulcal patterns can be used to compare and average data from corresponding cortical regions across subjects. Color coded maps are built to reveal statistical effects of covariates, such as diagnosis, gender, symptom scales and genotype on the gray matter deficits, and other cortical features that are mapped. Importantly, maps of individual differences in cortical shape and gray matter are stored separately and analyzed for systematic differences in disease.

This longitudinal study consisted of 108 MRI scans acquired at the Center for Magnetic Resonance in Queensland, one of several centers contributing MRI data to the atlas. Patients in this study had a re-scan interval of 3-4 years. Average maps were made for rates of volumetric loss, and changes in gray matter distribution over time.

diminish. Instead we use a warping approach to reinforce sulcal features in their mean geometric locations, as data from more patients is added to the atlas.

The first step in cortical averaging is to flatten a model of the cortex into a simpler, 2 parameter surface such as a sphere or a plane. A color code is retained to store 3D information on where each point came from in 3D, before being mapped to the flat map. In making a well-resolved average model of the cortex, for a group of subjects, we elastically warp the flat map

to match an average set of sulcal curves in flat space (see below, left). When this warp is applied to the color code that stores cortical locations, it places information at a given point in flat space, saying which gyral features correspond across all subjects. These locations are averaged together resulting in reinforcement of gyral features.

Warping fields store a measure of how much each subject deviates from the group average. The brown brain deviates from the gray brain (see above, figure a), which is a group average. The deformation field relates cortical measures in one subject and transfers them on the group average cortex, for comparison and integration. The amount of deformation required to make them match is shown in color (see above, figure b), pink colors are largest deformation. By saving these maps from many subjects, regions of greatest shape variability in the anatomy emerge: red colors show the perisylvian language cortex is the most variable region of the brain, in terms of sulcal pattern differences. Average asymmetries in the data also come out, here the right hemisphere is torqued forward, on average, relative to the left.

Map Gray Matter Deficits. Gray matter density is a useful measure that describes the amount of tissue segmenting as gray matter, shown in green in figure a below, in a small sphere centered at each cortical point. The warping fields that match each subject’s anatomy can be used to create an average map of gray matter distribution, and see how this changes over time. A group of subjects with mild AD (mean mini-mental score of 22 out of 30) have severe deficits, shown in red, in posterior temporal cortex. The average deficit in gray matter can be plotted as a percentage, or as asignificance map. The advantage of cortical pattern matching is the ability to localize these deficits relative to gyral landmarks, showing that some territory is comparatively spared relative to immediately adjacent tissue that might be severely affected.

Tensor Maps. A second type of data stored in the atlas is a set of tensor maps that show the rate of volumetric loss. By warping an earlier scan onto a later one, the pattern of deformation required to match the anatomy is differentiated with tensor operators

ResultsScans taken 3 to 4 years apart show a gentle loss of tissue in controls, around zero to 1% per year. At this mild stage of the disease, AD patients lose tissue faster, with 3-5% loss in frontal and cingulate regions. These loss patterns appear to follow architectonic boundaries and have different hemispheric asymmetries at different stages of the disease.

to show regions with fastest loss rates, here shown in blue.

This normal elderly subject lost tissue most rapidly in a small region of the hippocampal head. This is hard to appreciate in the raw data shown here, but is clear from the color maps.

The above figure shows the average profiles of hippocampal tissue loss in the patients and controls. Tissue loss in controls, between 3-7% per year, in the hippocampal head, is faster than their loss of cortical gray matter. The average loss rate is smaller though and is half to 2 percent annually. AD patients lose tissue faster, averaging 6% loss annually, in a shifting pattern. The posterior hippocampus is losing tissue fastest in this mild to moderate stage of AD, and the atrophic rates are encoded in the atlas can be stratified to reflect different population strata.

This mapping data on cortical loss and atrophic rates is one part of a growing atlas that correlates data on structural change with PET, cryo, histologic, and molecular data, as well as functional activation measures of deficits.