3D STATISTICAL ANALYSIS

Section: Behavioral/Systems Neuroscience

Senior Editor: Dr. Stephen Lisberger

DYNAMICS OF GRAY MATTER LOSS

IN ALZHEIMER’S DISEASE

1Paul Thompson, 1Kiralee M. Hayashi, 2Greig de Zubicaray, 2Andrew L. Janke, 2Stephen E. Rose, 3James Semple, 1David Herman, 1Michael S. Hong,

1Stephanie S. Dittmer, 2David M. Doddrell, 1Arthur W. Toga

1Laboratory of Neuro Imaging, Brain Mapping Division,

Department of Neurology, UCLA School of Medicine,

710 Westwood Plaza, Los Angeles, CA 90095, USA

2Centre for Magnetic Resonance, University of Queensland,

Brisbane, QLD 4072, Australia

3GlaxoSmithKline Pharmaceuticals plc, Addenbrooke's Centre for Clinical

Investigation, Addenbrooke's Hospital, Hills Road, CB2 2GG, Cambridge, UK

Submission to Journal of Neuroscience

Submitted: August 20, 2002

Revised (including reviewers’ changes): October 14, 2002

Abbreviated Title (under 50 characters):

DYNAMICS OF GRAY MATTER LOSS IN ALZHEIMER DISEASE

Text Pages: 26, Figures: 8, Tables: 0

Number of Words: Abstract: 233 (max. 250), Introduction: 485 (max. 500), Discussion: 1912

(after suggested additions).

Key Words: Alzheimer’s Disease, aging, dementia, MRI, brain mapping, imaging, longitudinal, cortex

Please address correspondence to:

Dr. Paul Thompson

(Room 4238, Reed Neurological Research Center)

Laboratory of Neuro Imaging, Dept. of Neurology,

UCLA School of Medicine

710 Westwood Plaza, Los Angeles, CA 90095-1769, USA Phone: (310) 206-2101 Fax: (310) 206-5518 E-mail:thompson@https://www.sodocs.net/doc/129549049.html,

Acknowledgments: This work was supported by research grants from the National Center for Research Resources (P41 RR13642), the National Library of Medicine (LM/MH05639), National Institute of Neurological Disorders and Stroke and the National Institute of Mental Health (NINDS/NIMH NS38753), GlaxoSmithKline Pharmaceuticals UK, and by a Human Brain Project grant to the International Consortium for Brain Mapping, funded jointly by NIMH and NIDA (P20 MH/DA52176).

DYNAMICS OF GRAY MATTER LOSS IN ALZHEIMER’S DISEASE

1Paul Thompson, 1Kiralee M. Hayashi, 2Greig de Zubicaray, 2Andrew L. Janke, 2Stephen E. Rose,

3James Semple, 1David Herman, 1Michael S. Hong, 1Stephanie S. Dittmer, 2David M. Doddrell,

1Arthur W. Toga

1Laboratory of Neuro Imaging, Brain Mapping Division, and UCLA Alzheimer Disease Center,

Department of Neurology, UCLA School of Medicine

2Centre for Magnetic Resonance, University of Queensland, Australia

3GlaxoSmithKline Pharmaceuticals plc, Cambridge, UK

ABSTRACT

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 4D 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), and correlated with progressively declining cognitive status (p<0.0006). Novel brain mapping methods visualized dynamic patterns of atrophy in 52 high-resolution MRI scans of 12 AD patients (age: 68.4±1.9 yrs.) and 14 elderly matched controls (age: 71.4±0.9 yrs.), 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 seen cross-sectionally 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%/year in AD versus 0.9±0.9%/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 and dementia populations.

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Introduction

Strategies to chart how Alzheimer’s disease spreads dynamically in the brain are vital for understanding how it progresses, and for mapping treatment effects. A dynamic map of AD could uncover the path of degeneration for different brain systems, and define a powerful biological marker for clinical trials. Such brain maps would visualize the order in which cortical systems are affected in living populations, and identify how changes correlate with cognitive decline.

This study used serial brain imaging and cortical mapping to visualize how AD spreads, spatially and temporally, in the brain. It reveals anatomically selective, stage-specific deficits. It also visualizes the sequence in which deficits appear. It also provides, in 3D, the first detailed, quantitative maps of cortical gray matter and whole brain changes over time in any disease.

In early AD, intraneuronal filamentous deposits, or neurofibrillary tangles (NFTs), accumulate within neurons. These deposits are composed of hyperphosphorylated tau-protein (Hulstaert et al., 1997). This cellular pathology disrupts axonal transport and induces widespread metabolic decline. The resulting neuronal loss is observable as gross atrophy on MRI. Temporo-parietal association cortices and the medial temporal lobe are severely atrophied in AD (DeCarli et al., 2000), with the entorhinal cortex and hippocampus the earliest, and most severely, affected (Janke et al., 2001; Thompson et al., 2001). Profound atrophy is also observed in the posterior cingulate gyrus and adjacent precuneus. Specific atrophic patterns differentiate AD from frontotemporal, s emantic, and Lewy body dementias (O’Brien et al., 2001; Studholme et al., 2001). AD patients show minimal primary visual, sensorimotor, and frontal atrophy until late in the disease. Prior to symptom onset in AD, and also in those at genetic risk, gray matter loss is detectable in the anterior hippocampal/amygdala region (Lehtovirta et al., 2000; Reiman et al., 2001).

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Of particular interest is the temporal sequence of deficits in AD, as they spread across cortex. Braak and Braak (1997) noted at autopsy that NFT distribution was initially restricted to entorhinal cortices, spreading to higher order temporo-parietal association cortices, then frontal, and ultimately primary sensory and visual areas (cf. Delacourte et al., 1999; Price and Morris, 1999).

We set out to determine whether a similar wave of cortical atrophy could be mapped in patients while they were alive. The goal was to visualize the disease’s transit within cortex and relate it to cognitive decline. Recently, we used dynamic brain mapping to uncover the trajectory of cortical change as schizophrenia develops in the teenage brain (Thompson et al., 2001), and as normal adolescents lose gray matter (Sowell et al., 2001). In dementia, we expected a similarly selective profile of brain changes, instead emerging from temporal cortices, sparing primary cortices until late in the disease. We also hypothesized (1) that frontal and association cortices would be progressively enveloped as cognitive function declined, and (2) that the pathology would evolve differently in each brain hemisphere, with the left hemisphere engulfed earlier, and more severely, than the right.

Methods

Subjects

Over a time interval of 3 years, we used longitudinal MRI scanning (2 scans: baseline and follow-up) and cognitive testing to study a group of AD subjects as their disease progressed. A second, demographically-matched, group of healthy elderly control subjects was also imaged longitudinally (two scans) as they aged normally. The AD subject group consisted of 12 patients scanned twice (6 males/6 females; mean age ±s.e. at first scan: 68.4±1.9 yrs.; at final scan: 69.8±2.0 yrs.; mean interval between first and last scans: 1.5±0.3 yrs.). These patients were diagnosed with AD using DSM-IV criteria (Diagnostic and Statistical Manual of Mental Disorders) and had a typical clinical presentation. They also fulfilled NINCDS-ADRDA criteria for probable AD (McKhann et al., 1984; National Institute of Neurological Disorders and Stroke/Alzheimer’s Disease and Related Disorders Associat ion). All patients were re-assessed at intervals of 3-6 months with a full clinical evaluation and their cognitive status was evaluated using the Mini-Mental State Exam (MMSE; Folstein, 1975). During the study, their cognitive status declined rapidly from an initial MMSE score of 17.7±1.9 to 12.9±2.5 (mean change: 5.5±1.9 points; p < 0.00054, one-tailed t-test; maximum score 30). This corresponds approximately to a transition from moderate to severe AD. At the same time, a group of 14 healthy elderly controls was scanned twice (7 males/7 females; age at first scan: 71.4±0.9 yrs.; at final scan: 74.0±0.9 yrs.; mean interval

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between first and last scans: 2.6±0.3 yrs). During the study, the control subjects’ cognitive status remained stable. Their MMSE was 29.5±0.3 at both baseline and follow-up, with no change.

Exclusion criteria for the two groups included the presence of white matter lesions (WMLs) on T2-weighted MRI scans, pre-existing psychiatric illness or head injury, and history of substance abuse or depression as measured by the Geriatric Depression Scale (GDS; Yesavage et al., 1988). All subjects were right-handed. Informed consent was obtained from all participants before scanning. These samples, both AD and control, consisted of entirely different subjects from the group of AD patients and controls from whom data was reported in our previous cross-sectional study (Thompson et al., 2001). Three disease stages were effectively mapped in this new, longitudinal sample. These broadly correspond to healthy aging, and the transition from moderate to severe AD (with MMSE scores 29, 18 and 13, respectively).

MRI Scanning

Patients and controls were identically scanned, with the same scanning protocol over time. Each subject had 2 MRI images separated by more than a year. Images were acquired on a 2 Tesla Bruker Medspec S200 whole body scanner at the Centre for Magnetic Resonance, University of Queensland, Australia. A linearly polarized birdcage head-coil was used for signal reception. 3D T1-weighted images were acquired with an inversion recovery segmented 3D gradient echo sequence (known as MP-RAGE: Magnetization Prepared RApid Gradient Echo) to resolve anatomy at high resolution. Acquisition parameters were: TI/TR/TE = 850/1000/8.3 ms, flip angle = 20o, 32 phase-encoding steps per segment, and a 23 cm field of view (FOV). Images were acquired in an oblique plane perpendicular to the long axis of the hippocampus (Jack et al., 1998), with an acquisition matrix of 256?256?96, and zero-filled to 2563.

Image Processing and Analysis

Serial images acquired across the 2-year time-span were processed as follows. Briefly, for each scan, a radio-frequency bias field correction algorithm eliminated intensity drifts due to scanner field inhomogeneity, using a histogram spline sharpening method (N3; Sled et al., 1998). Images were then normalized by transforming them to a standard 3D stereotaxic space, in a two-step process that retained information on brain change over time. First, each initial T1-weighted scan was linearly aligned (registered) to a standard brain imaging template (the International Consortium for Brain Mapping non-linear average brain template, ICBM152; Evans et al., 1994) with automated image registration software (Collins et al., 1994). Follow-up scans were then rigidly aligned to the baseline scan from the same subject (Collins et al., 1994). These mutually registered scans for each patient were then linearly mapped into ICBM space by

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combining the intra-patient transform with the previously computed transform to stereotaxic space.

Tissue Maps

To equalize image intensities across subjects, registered scans were histogram-matched. A supervised tissue classifier generated detailed maps of gray matter, white matter, and cerebro-spinal fluid (CSF). Briefly, 120 samples of each tissue class were interactively tagged to compute the parameters of a Gaussian mixture distribution that reflects statistical variability in the intensity of each tissue type (Zijdenbos and Dawant, 1994). A nearest-neighbor tissue classifier assigned each image voxel to a particular tissue class (gray, white or CSF), or to a background class (representing extracerebral voxels in the image). The inter/intra-rater reliability of this protocol, and its robustness to changes in image acquisition parameters, have been described previously (Sowell et al., 1999). Gray and white matter maps were retained for subsequent analysis.

3D Cortical Maps

A surface model of the cortex was automatically extracted (MacDonald et al., 2000) for each subject and time-point, as described in our previous studies (Thompson et al., 2001). This software creates a mesh-like surface which is continuously deformed to fit a cortical surface tissue threshold intensity value from the brain volume. The software was modified to permit high-resolution extraction of both the lateral and medial hemispheric surfaces, including the cingulate, primary visual cortex and corpus callosum (Fig. 1). The intensity threshold was defined as the MRI signal value that best differentiated cortical CSF on the outer surface of the brain from the underlying cortical gray matter.

Cortical Pattern Matching

An image analysis technique, known as cortical pattern matching, was used to better localize disease effects on cortical anatomy over time, and increase the power to detect systematic changes. The approach models, and controls for, gyral pattern variations across subjects. It visualizes average maps of cortical change in a population, and encodes their variance and group differences.

Based on cortical models for each subject at different time-points, a 3D deformation vector field was computed measuring shape change in the brain surface across the time interval (Thompson et al., 2001). This directly accommodates any brain shape changes when comparing cortical gray matter within a subject across time. The

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deformation reconfigures the earlier cortex into the shape of the later one, matching the entire gyral patterns and cortical surfaces in the pair of 3D image sets.

Matching Cortical Anatomy across Subjects. As well as the deformation that matches anatomy over time, a second deformation was computed that matches gyral patterns across all the subjects in the study. This allows data to be averaged and compared across corresponding cortical regions (the algorithm for this is described in Thompson et al., 2000). As shown in Fig. 1, a large set of 72 sulcal landmarks per brain is used to constrain the mapping of one cortex onto another. This associates corresponding cortical regions across subjects. An image analyst (KMH) who was blind to subject diagnosis, gender and age, traced each of 30 sulci in each hemisphere on the surface rendering of each subject’s brain (13 on the medial surface, 17 on the lateral surface). On the lateral brain surface these included: the Sylvian fissure, central, pre-central, and post-central sulci, superior temporal sulcus (STS) main body, STS ascending branch, STS posterior branch, primary and secondary intermediate sulci, inferior temporal, superior and inferior frontal, intraparietal, transverse occipital, olfactory, occipito-temporal, and collateral sulci. On the medial surface these included: the callosal sulcus, the inferior callosal outline, the paracentral sulcus, anterior and posterior cingulate sulci, the outer segment of a double parallel cingulate sulcus (where present; see Ono et al., 1990), the superior and inferior rostral sulci, the parieto-occipital sulcus, the anterior and posterior calcarine sulci, and the subparietal sulcus. In addition to contouring the major sulci, a set of 6 midline landmark curves bordering the longitudinal fissure was outlined in each hemisphere to establish hemispheric gyral limits. Spatially registered gray-scale image volumes in coronal, axial, and sagittal planes were available simultaneously to help disambiguate brain anatomy. Landmarks were defined according to a detailed anatomical protocol (Sowell et al., 2001; Hayashi et al., 2002; cf. Steinmetz et al., 1990; Leonard, 1996) based on the Ono sulcal atlas (Ono et al., 1990). This protocol is available on the Internet (Hayashi et al., 2002) and has known inter- and intra-rater reliability, as previously reported (Sowell et al., 2001).

Average Cortical Model Construction. To create an average 3D cortical model for each group of subjects (e.g. elderly normal, or AD), the following steps were employed (see Figs. 1 and 2, and Thompson et al., 2001 for details). For each subject, all sulcal/gyral landmarks (Fig. 1(b),(c)) were flattened into a 2D plane along with the cortical model (e). Technical issues of minimal distortion and the computation of these flat mappings are addressed in (Thompson et al., 2001). A color code (f) retains the original 3D position of each cortical point as a {red,green,blue} color triplet plotted in the 2D parameter space (f).Once data are in this flat space, sulcal features are aligned across subjects with a warping technique. To illustrate this process,Fig. 2(a) shows part of one subject’s flat map, and its corresponding color code [Fig.

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2(c)]. An average set of sulcal curves, derived from many subjects, is overlaid on the flat map (a). These maps are warped (b) so that individual sulcal features in them are driven into correspondence with the average set of sulcal curves. Fig. 2(d) shows how this warping process affects a regular grid ruled over an individual color-coded flat map. The warped color images (d) from many subjects are averaged together pixel-by-pixel and decoded; mathematically, it can be shown that this image averaging creates a crisp average cortical model with gyral features in their mean anatomic locations (Fig. 2(f); Thompson et al., 2000). The point of this procedure is that the computational matching of sulci avoids destructive cancellation of features (cf. Fischl et al., 1999). This cancellation happens if images are directly averaged together [Fig. 2(e)]. Common features, reinforced in the group average, appear in their group mean anatomic locations (f). Importantly, local measures of gray matter density (see below, and Fig. 3) may be convected along with these warps and plotted on the average cortex, prior to statistical analysis. Confounding effects of cross-subject anatomical variance are greatly reduced, empowering detection of disease effects.

Averaging Cortical Gray Matter Maps. Given that the deformation maps associate cortical locations with the same relation to the primary folding pattern across subjects, a local measurement of gray matter density was made in each subject and averaged across equivalent cortical locations. To quantify local gray matter, we used a measure termed ‘gray matter density’ which has been used in many prior studies to compare the spatial distribution of gray matter across subjects (Wright et al., 1995; Bullmore et al., 1999; Sowell et al., 1999; Ashburner and Friston, 2000; Rombouts et al., 2000; Mummery et al., 2000; Thompson et al., 2001a,b; Good et al., 2001; Baron et al., 2001). This measures the proportion of gray matter in a small region of fixed radius (15 mm) around each cortical point. Given the large anatomic variability in some cortical regions, high-dimensional elastic matching of cortical patterns (Thompson et al., 2000, 2001) was used to associate measures of gray matter density from homologous cortical regions first across time, and then also across subjects (as shown in Fig. 3). One advantage of cortical matching is that it localizes deficits relative to gyral landmarks; it also averages data from corresponding gyri, which would be impossible if data were only linearly mapped into stereotaxic space. Annualized 4D maps of gray matter loss rates within each subject were elastically realigned for averaging and comparison across diagnostic groups (Fig. 7).

Mapping Gray Matter Loss

Statistical maps were generated indicating locally the degree to which gray matter loss rates were statistically linked with diagnosis and cognitive performance (MMSE scores; Fig. 6). To do this, at each cortical point, a multiple regression was run to assess whether the gray matter loss rate at that point depended on the covariate of interest (e.g. test scores,

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diagnosis). The p-value describing the significance of this linkage was plotted on at each point on the cortex using a color code to produce a statistical map (e.g., Figs. 4,6,8).

Permutation Testing. Maps identifying these linkages were computed pointwise across the cortex and assessed statistically by permutation. We preferred this to using an analytical null distribution to avoid assuming that the smoothness tensor of the residuals of the statistical model were stationary across the cortical surface. The presence of significant effects in maps of statistics can typically be tested using parametric inference based on Gaussian random field theory (Friston et al., 1996), or by non-parametric (e.g., permutation) methods, both of which have been applied widely in functional (Holmes et al., 1996) and structural brain imaging (Bullmore et al., 1999; Sowell et al., 1999). We used permutation here to avoid making assumptions about the spatial covariance of the residuals (Nichols and Holmes, 2002), and to avoid complex corrections for data localized on surfaces (see Thompson et al, 2000 for a discussion of this issue). Permutation methods, used here, measure the distribution of features in statistical maps (such as the area with statistics above a predetermined threshold) that would be observed by accident if the subjects were randomly assigned to groups. This computed distribution is then used to compare the features that occurred in the true experiment with those that occurred by accident in the random groupings. A ratio is computed describing what fraction of the time an effect of similar or greater magnitude to the real effect occurs in the random assignments. This is the chance of the observed pattern occurring by accident. This fraction provides an overall significance value for the map (corrected for multiple comparisons; Nichols and Holmes, 2002).

To define a corrected significance value (i.e., an overall p-value) for a map, we thresholded all significance maps at a primary threshold of p=0.01 and measured the total area of the cortex with statistics more significant, at a voxel level, than this threshold (as in earlier work, e.g., Sowell et al., 2001). This threshold (p=0.01) was chosen a priori based on our earlier work in an independent, cross-sectional sample (Thompson et al., 2001), to optimize detection of broad, diffuse effects. The prior study suggested that large areas of cortex would be diffusely affected at a voxel-wise significance level stro nger than 0.01. The ‘total area’ of the suprathreshold regions on the surface was computed. This area measure was used in the permutation tests to control of number of false positives per map. A ‘total suprathreshold area’ statistic was chosen rather than a measure of ‘cluster extent’ or ‘peak height’ (Friston et al., 1996) to sensitize the permutation test to the detection of subtle but diffuse effects that occur over large (and possibly disconnected) regions of cortex. Despite selection of a low significance threshold at the voxel level to define clusters, the overall P-value for the map still controls for Type I error, as the threshold to define significant clusters is the same in the real and in the null

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simulations from which the permutation distribution is drawn. The total area of the average surface with suprathreshold statistics was used rather than the number of surface vertices with suprathreshold statistics as the total area is invariant to the sampling density of points on the surface (so long as the surface is sufficiently highly sampled).

Control for Multiple Comparisons. In this type of statistical map, the number of tests that are computed is 65,536 per hemisphere, as this is the number of vertices in the surface model of each cortical hemisphere. However, these tests are highly correlated, and the spatial covariance of their residuals is incorporated into the null distribution, and this controls for the number of independent tests (Thompson et al., 2000).

Specification of a Region of Interest. Because we expected highly significant effects, we did not in general restrict the anatomical search space for the permutation tests, as is quite common in functional imaging (Friston et al., 1996). Permutations were conducted over the whole hemisphere (this results in conservative control over Type I error, as features anywhere on the cortex enter the null distribution). Left and right hemispheres were assessed separately. In one case, we assessed whether the medial surface was affected. To do this we created a volume of interest (or search region) that contained the medial hemispheric surface only, using the surface lines traced at the interhemispheric margin to define its boundary on the average surface. Suprathreshold effects in this cortical region were then assessed to compute a permutation distribution on their total area, and an overall corrected P-value was derived for the medial wall effects.

In each case, the covariate vector was permuted 1,000,000 times on an SGI RealityMonster supercomputer with 32 internal R10000 processors, and a null distribution was developed for the area of the average cortex with statistics above a fixed threshold (p<0.01) in the significance maps. (Post hoc tests revealed that the corrected P values were robust to differences in the choice of this primary threshold).An algorithm was then developed to report the significance probability for each map as a whole (Thompson et al., 2000, 2001), so the significance of the loss patterns could be assessed after the appropriate correction for multiple comparisons. Separate maps were made to show average rates of loss (e.g. Fig. 6) and the significance of this loss in patients relative to controls (Fig. 4(a),(b)).

Results

Mapping Average Gray Matter Deficits in AD. In AD patients, a highly significant gray matter deficit was observed in a broad anatomical region encompassing temporal and parietal cortices bilaterally (see Fig. 4, top row, (a); p< 0.00495 for an effect of diagnosis, Left hemisphere; p < 0.0154, Right hem.; permutation tests). The most significant

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impairments occurred in temporal and parietal regions (red colors, Fig. 4(a)), where deficits exceeded 15% (Fig. 4(c)). More intriguing was the anatomical specificity of the loss. A sharp division occurred in the loss maps (Fig. 4(b); blue colors), with central and post-central gyri displaying minimal loss compared with the parietal association cortices immediately posterior to them. Primary sensory and motor cortices were comparatively spared in the disease (with a 0-5% deficit on average in the central and postcentral gyri; labeled S/M in Fig. 4(b)).

The bottom row (Fig. 4(e)-(h)) shows the average deficit pattern in AD 1.5 years later, after a rapid decline in MMSE score from 17.7±1.9 to 12.9±2.5 (p< 0.00054). Again the cortex exhibits substantial gray matter loss, with deficits intensifying still further in the most severely affected regions (Fig. 4(e),(f); p < 0.00027 for an effect of diagnosis, Left hemisphere; p< 0.00056, Right hem.; permutation tests). Two features are apparent: (1) the frontal cortices, initially only mildly affected (with 6-10% loss), are now severely affected (deficits exceeded 15% everywhere; Fig. 4(g)); and (2) the sensory and motor territory is still comparatively spared (blue colors, Fig. 4(f)), despite the frontal spread of the deficits.

Dynamic (4D) Maps. The time-course of these gray matter losses, as they emerge over a period of cognitive decline lasting 1.5 years, is observed in the accompanying video sequences (see Supplementary Data, https://www.sodocs.net/doc/129549049.html,/~thompson/AD_4D/dynamic.html). These 4D data were computed from the maps with a previously described algorithm (Thompson and Toga, 1996). The transit of deficits from temporo-parietal into frontal cortices is dramatic, and so is the sparing of sensorimotor areas.

Medial Wall Effects. G iven the interest in early limbic changes in AD, we also mapped the loss pattern across the medial wall of the brain hemispheres (Fig. 5). Again, sulcal pattern matching was used to pool information from corresponding cortical regions (e.g. cingulate) across subjects, and to create the average maps of gray matter deficits in AD.

In the left hemisphere, the entire medial cortex was in deficit at the first scan (Fig. 3(b)). Intriguingly, in the right hemisphere, the anatomy was segregated into three major systems (Fig. 5(a)): (1) greatest impairment (>15%) was observed in the temporal/entorhinal regions and the parietal lobule, and, perhaps surprisingly, the adjacent occipital/visual cortices; (2) the cingulate/paralimbic belts (10-15% loss) were significantly, but less severely affected (yellow colors, Fig. 5(a)), (3) frontal regions, and in particular orbitofrontal regions (labeled OF, Fig. 3(a)) were

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comparatively spared (0-5% loss; blue colors). The left hemisphere was significantly more severely impaired than the right (p<0.029 for disease-specific asymmetry; corrected P < 0.0018, L hem., and corrected P < 0.014, R hem., for the effect of diagnosis at the initial scan on the medial wall; permutation tests). Most left hemisphere regions showed a >15% deficit relative to healthy controls (Fig. 3(b)), consistent with our earlier cross-sectional studies in an independent sample (Thompson et al., 2001). Both left and right hemisphere maps suggested that limbic regions are more intensely impaired than frontal cortices in AD. This remained true even late in the disease when multiple systems are severely affected (Fig. 5(c)). After a sharp drop in MMSE score from 18 to 13, the majority of the cortex was engulfed (Fig. 5, bottom row; corrected P < 0.00015, L hem., and corrected P < 0.00015, R hem., for the effect of diagnosis on the medial wall; permutation tests). Even then, some cortical regions were mostly intact (blue colors, Fig. 5(g)), specifically in the frontal and sensorimotor cortices. A frontal band (0-5% loss) was sharply delimited (Fig. 5(c)) from the limbic and temporo-parietal regions that showed severest deficits in AD (>15% loss). This pattern is consistent with the hypothesis that AD pathology spreads centrifugally from limbic/paralimbic to higher-order association cortices (Mesulam, 2002).

Cognitive Correlates. It was also important to confirm that these structural differences were functionally significant. To do this, we tested whether gray matter differences linked with differences and declines in cognitive performance, as quantified by MMSE scores. MMSE scores were lower in the AD group than in controls both initially (p < 2.85x10-7) and after 1.5 years (p < 2.6x10-8). When rates of change were considered, MMSE decline over time was highly significant in AD (p < 0.00054), while no changes were detected in the controls (mean change: 0.0; p > 0.05).

Highly significant linkages were found relating lower cognitive scores to greater gray matter deficits (Fig. 6). These correlations were observed in all brain regions where there was significant loss (Fig. 6(a)-(d)), including the temporal-parietal and limbic cortices. Linkages were also found between frontal gray matter reduction and lower MMSE, but only at the later time-point when frontal gray matter was in significant deficit (Fig. 6(e)). As expected, no correlations were found between gray matter differences in sensory and motor cortices and cognitive performance (blue colors, Fig. 6(b), labeled S/M). These maps support the idea that structure/cognition effects are regionally specific in AD, at least initially (Fig. 6(b)). Correlations were strongest in regions with greatest average loss (left cingulate and left temporal and parietal cortices, Fig. 6(d)). These correlation maps were confirmed to be significant by permutation (p < 0.0013, L hem., p < 0.0028, R hem. when mean MMSE was 18, and p < 0.0028, L hem., p < 0.0047, R hem., when mean MMSE had declined to 13).

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Mapping Rates of Loss. The availability of repeat scans from the same subjects allowed rates of tissue loss to be computed locally for each subject and each location on the cortex. These changes are shown as map (Fig. 7). This illustrates the group average rate of gray matter loss across the cortical surface, in patients and controls. Even the healthy controls showed a trend for diffuse gray matter loss (Fig. 7(a)-(d); p < 0.08), at an annual rate of 0.91±0.92%/year overall (L hem.: 1.16±1.41%/year; R hem.: 0.67±1.25%/year). However, very few regions exceeded 1% annual gray matter loss (Fig. 7, top row). This approximates the tissue loss rate seen in normal adolescence (Thompson et al., 2001). The anatomy of the healthy controls therefore remained relatively stable, while their MMSE score remained unchanged at 29.5±0.3.

AD patients lost gray matter significantly (p<0.05 for overall annual loss of gray matter; Fig. 7(e)-(h)), and at a significantly more rapid rate than controls (p<0.042), with a total gray matter loss rate of 5.03±2.28%/year (L hem.: 5.43±3.29%/year; R hem.: 4.64±3.31%/year). Regions with a prominent 4-5% annual loss included the right cingulate, temporal and frontal cortices bilaterally (Fig. 7, bottom row). The loss rate patterns were also more spatially diffuse than the deficit maps at baseline and follow-up (Figs. 4 and 5). In summary, the leading edge of the region with significant deficits spreads somewhat centrifugally (from medial temporal/limbic to frontal regions). Nonetheless, the loss rate maps show progressively intensifying deficits at the leading edge (frontal cortex) as well as in regions that are already severely affected, e.g., the lateral surfaces of the temporal lobes.

Loss Rate Asymmetries. In AD, the left hemisphere lost gray matter faster than the right (0.79%/year faster; p<0.04). This is consistent with prior reports of the left hemisphere being more severely affected in AD, both metabolically and structurally (Loewenstein et al., 1989; Friedland and Luxenberg, 1988; Janke et al., 2001; Johnson et al., 1998; Thompson et al., 2001). A trend for faster gray matter loss in the left hemisphere was also observed in controls (0.50%/year faster; p<0.057).

Logically, if an asymmetric loss process had already been occurring for many years, one might expect the left hemisphere to have significantly less gray matter than the right at the baseline scan, in both patients and controls. This was confirmed to be the case. Fig. 8 shows the average gray matter asymmetry in the controls, with a prominent sensorimotor region showing 15-20% less gray matter in the left hemisphere (Fig. 8(a)), compared with its counterpart on the right. These maps were created by subtracting the gray matter map in the left hemisphere from that on the right, in each individual, before averaging data from corresponding cortical regions across subjects. To account for gyral pattern

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asymmetries (Geschwind and Levitsky, 1968), this subtraction was performed after computing an additional warp in the cortical parameter space to align each subject’s left and right gyral patterns. Both controls (p < 0.0069; Fig. 8(c),(d)) and patients (p < 0.0091) had significantly less cortical gray matter in the left hemisphere. In AD, there was also an increasing asymmetry, over time (p < 0.028), in the overall quantity gray matter (including deep nuclei as well as cortex). This was not detected in controls. In AD, the left hemisphere had 2.4%(±2.0) less gray matter overall than the right at baseline, and 3.5%(±2.0) less gray matter at follow-up.

This indicates faster left hemisphere cortical degeneration in AD, at least during the time interval observed in this study. When the components of this gray matter asymmetry are factored apart, the loss process in AD therefore occurs (1) at an asymmetric rate (left faster than right), and (2) on top of a prevailing gray matter asymmetry (left less than right) seen in AD and even in healthy elderly subjects.

Whole Brain Atrophic Rates. Since cortical gray matter maps discriminated patients from controls so strongly (with p < 0.005 initially and p < 0.0005 at follow-up; permutation tests), we wanted to test whether simpler measures would also reveal disease-related differences or rates of change. The goal was to compare volumes and maps to see which detected losses most effectively.

Whole brain atrophic rates (i.e. the loss rate for total cerebral volume) were found to discriminate patients and controls. Total cerebral volume was computed from the cortical models (Fig. 1), and its annual rate of change was computed for all subjects in the study. Overall cerebral volume loss rates were significantly faster in AD than in controls, with a loss rate of 5.22±2.04%/year in AD (p < 2.3x10-5), compared to 0.88±0.15%/year in controls (p < 0.013 for significant loss in controls, p < 0.003 for group difference; both hemispheres pooled). The left hemisphere lost volume at 5.86±8.60%/year in AD, compared with 0.99±0.59% in controls (p < 0.019 for significant loss in controls, p < 0.023 for group difference), while the right hemisphere lost volume at 4.57±5.61%/year in AD, compared with 0.88±0.46% in controls (p < 0.0083 for significant loss in controls, p < 0.0095 for group difference; all tests one-tailed). These disease effects were found in multiple regressions that controlled for age and gender (although effects of these covariates were not significant; overall multiple R=0.48, p<0.006). A trend for faster left hemisphere loss also was observed for cerebral volume (p < 0.055), in line with the significantly faster gray matter loss rate mapped in the left hemisphere.

To determine whether the cerebral volume loss rate was attributable primarily to gray or white matter degeneration, we

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also evaluated white matter loss rates. We did not find a faster white matter loss rate in AD relative to controls (p=0.41), although both groups lost white matter significantly over time (p < 0.035). The white matter loss rate in AD (3.30±2.06%/year; L: 3.20±2.91%/year, R: 3.40±3.03%/year) was comparable to that in controls (2.72±1.44%/year; L: 2.91±2.09%/year, R: 2.52±2.07%/year), and no asymmetries were detected (p>0.1).

Discussion

This study used new longitudinal brain mapping techniques to chart the transit of structural deficits across the cortex in Alzheimer’s disease. When the deficit data are visualized in 4D, a dynamic, spreading wave of loss is observed. The left hemisphere was engulfed fastest, with the right following a similar sequence after a time lag. In the right hemisphere, sharp boundaries appeared in the deficit patterns at the cingulate/frontal border on the medial wall (Fig. 5(c)), indicating differential susceptibility, at least transiently, in mild to moderate AD. Regionally selective atrophy was found in distinct disease phases, with initial sparing of frontal, and then only sensorimotor cortices. Deficits and rates of change were coupled with declining cognitive status, quantified by MMSE scores. As a supplement to clinical measures of disease progression, which can be notoriously variable (DeCarli et al., 2000), cortical maps quantitatively store information on changes expected in AD, offering a standard against which drug effects can be calibrated.

The path of disease progression is appreciated most clearly in the video sequences (see Supplementary Data). These suggest a spatially complex model of different atrophic patterns as AD progresses. Three main features are observed: (1) the overall deficit pattern spreads through the brain in a temporal-frontal-sensorimotor sequence, with a time lag in the right hemisphere; (2) the left hemisphere degenerates faster than the right: this asymmetric loss rate increases the existing asymmetry in cortical gray matter (L

Pathology. The deficit sequence also matches the trajectory of neurofibrillary tangle distribution observed post mortem, in patients with increasing dementia severity at death (Braak and Braak, 1997). Consistent with the deficit maps observed here, NFT accumulation is minimal in sensory and motor cortices, but occurs preferentially in entorhinal pyramidal cells, the limbic periallocortex (layers II/IV), the hippocampus/amygdala and subiculum, the basal forebrain cholinergic systems and subsequently in temporo-parietal and frontal association cortices (layers III/V; Pearson et al.,

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1985; Arnold et al., 1991). Neuropathologic studies also reveal that cortical layers III and V selectively lose large pyramidal neurons in association areas (Brun and Englund, 1981). Immunocytochemical studies report 11%-50% synaptic loss in superior temporal and inferior parietal cortices in AD (Clinton et al., 1994). A heavy loss of cholinergic axons occurs, with a variable decrease in cholinoreceptive pyramidal neurons (Mesulam, 2000). Gray matter deficits may indicate a depletion in cholinoreceptive neurons, and perhaps a reduced receptiveness to cholinergic therapy (cf. Hampel et al., 2002). Gomez-Isla et al. (1997) noted that in AD, both neuronal loss and neurofibrillary tangle density were correlated and increased in parallel with the duration and severity of illness, while the number of senile plaques and amyloid burden in the superior temporal sulcus were not related to neuronal loss, number of neurofibrillary tangles, or duration of disease.

Gray matter atrophy observed with MRI may also be attributable to a combination of processes other than, or in addition to, neuronal loss, including cell shrinkage, reduced dendritic extent, and synaptic loss (see McEwen, 1997, and Uylings and de Brabander, 2002 for recent reviews). In healthy aging, age-related neuronal loss does not occur in most regions of the neocortex (Terry et al., 1987; Morrison and Hof, 1997), and appears specific to the frontal cortex (de Brabander et al., 1998) and some hippocampal regions (e.g., CA1 and the subiculum; Simic et al., 1997). By contrast, marked neuronal loss occurs in early AD (Gomez-Isla et al., 1997), with severe early losses in layer II of the entorhinal cortex. Normal age-related cortical changes may be due in part to cell shrinkage (Shimada, 1999), reduced dendritic length (Flood et al., 1987; Hanks and Flood, 1991), as well as changes in perfusion, fat and water content and other chemical constituents (Weinberger and McClure, 2002). Age-related dendritic reduction may be region- and lamina-specific (Uylings and Brabander, 2002). Nakamura et al. (1984) found greatest reductions in layer V pyramidal basal dendrites with normal aging, and dentate granule cells also display significantly reduced apical dendritic length (>40% in the dentate gyrus; Hanks and Flood, 1991). In summary, changes observed here in normal aging may primarily reflect cell shrinkage, reductions in dendritic extent, and synaptic loss; the changes in AD may reflect a combination of these processes as well as substantial neuronal loss(Gomez-Isla et al., 1997).

Metabolism. Metabolic scans in AD, acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), show a similar deficit pattern. Decreased metabolic activity is found in temporal and parietal lobes and in the posterior cingulate cortices (Mazziotta et al., 1992; Mega et al., 1997). Frontal deficits typically occur later. These decreases predict cognitive decline rate (on the MMSE) and also survival (Jagust et al., 1996). A recent review of vascular and perfusion changes in AD (de la Torre, 2002) noted that cerebral hypoperfusion typically predates cortical

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hypometabolism in Alzheimer’s disease. Microvascular changes may therefore contribute to changing cortic al metabolism and neocortical atrophy in AD, as these changes progress in a similar sequence.

Interestingly, gray matter loss at autopsy is predominantly cortical in Alzheimer's patients under 80 years of age (Hubbard and Anderson, 1981). This induces corpus callosum (Thompson et al., 1998, Hampel et al., 2002) and thalamic atrophy (Jernigan et al., 1991) leading to a widespread cortical disconnection syndrome. The transition of AD pathology into frontal association cortices suggests a degeneration of synaptically linked cortical pathways, with a relative sparing of phylogenetically older, sensorimotor, cortices. Occipital regions are also atrophic in this study; changes are not always detected in metabolic or perfusion studies of AD, where visual cortices often serve as a control region in PET or SPECT imaging studies (Jagust et al., 1996).

Differential Susceptibility. The anterior and ventromedial temporal lobes may be especially susceptible to AD pathology. The vulnerability of neocortical association areas may relate to their degree of functional connectivity with limbic structures, where pathology begins (Arriagada, 1992). As noted in a recent physiological model (Mesulam, 2002), increased expression and phosphorylation of tau occurs in regions with high levels of neuroplasticity (Brion et al., 1994). This risk factor for NFT formation disrupts the cytoskeleton and ultimately leads to cell death. The spread of NFT pathology (Morrison and Hof, 2002) may originate in limbic regions due to their high levels of baseline plasticity. Later cell loss in association areas may result from cortical remodeling due to impaired input activity in limbic-paralimbic neurons that innervate them.

Cortical Specificity. In the deficit maps, some barriers to disease progression appear at architectonic boundaries. The sensorimotor division is clearest (Fig. 4(b),(f), and Fig. 6(b)); the right cingulate sulcus (Fig. 5(c)) also delimits spared frontal from severely affected limbic cortex. These barriers may be transient, but they suggest that structural deficits may differ sharply on either side of known architectonic boundaries, a feature seen in our earlier studies (Thompson et al., 2001).

Detection Power. Practical questions arise as to what is the most powerful measure to discriminate AD from healthy aging, and for resolving treatment effects. Here gray matter maps, at a single time point, better discriminated AD (p < 0.00027 at follow-up) than longitudinal loss rates for total cerebral volume (p < 0.003), and rates of overall gray matter loss (p < 0.04). All these measures correlated significantly with cognitive decline. Brain volume change rates, derived

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from high-resolution cortical surface models (Fig. 1), may be more effective at discriminating AD than total gray matter changes, which may be more susceptible to partial volume error. Map-based measures showed vastly greater effect sizes than any measures based on volumes (e.g. p < 0.00027 at follow-up). Increased power may result from restricting the search space for disease effects to the cortical sheet. Permutation tests on cluster size (Bullmore et al., 1999; Thompson et al., 2001) in the significance maps also sensitize the maps for detecting disease effects.

Advantages of this Study. This study builds on prior work on AD progression (Ashburner et al., 2002; DeCarli et al., 2000). Advantages of this study over prior work are that advancing deficits are shown in the form of dynamically changing maps. Cortical pattern matching, a technique used here, also relates deficits to gyral anatomy (e.g. the cingulate/frontal division, Fig. 5(c)). The image analysis method involves a high-dimensional registration followed by a test of gray scale differences and is therefore a hybrid in terms of the continuum between deformation morphometry and voxel-based morphometry previously discussed in the literature (Bookstein, 2001; Ashburner and Friston, 2001). As advocated elsewhere (Bookstein, 2001), high-dimensional warping is used to align structures across subjects before comparing gray matter differences. This can increase detection power by reducing the anatomical variance present in image subtraction methods (Fig. 1). Asymmetries and group effects can then be mapped using surface-based statistics, after explicitly modeling cortical pattern differences across hemispheres, subjects, and time.

Relation to Prior Work. Recent techniques to map AD progression use serial image alignment (Woods et al., 1993; Fox et al., 1996; Subsol et al., 1997; Wang et al., 2002), sometimes in conjunction with image deformation techniques (Freeborough and Fox, 1998; Thompson et al., 2000; Janke et al., 2001; Scahill et al., 2002). These techniques produce an overall measure of change (e.g., brain volume loss in percent; Fox et al., 1999; Wang et al., 2002) or detailed maps of these changes (Janke et al., 2001; Thompson et al., 2000). Four-dimensional maps of degenerative rates may also be derived from a deformation field that elastically transforms a subject's anatomy from its earlier configuration to its later shape (Janke et al., 2001). Using a ‘brain boundary shift integral’ technique, Fox et al. (1999, 2000) noted that yearly rates of overall brain atrophy, based on their measures of total cerebral volume, was 2.4±1.1%/year in AD, and 0.4±0.5%/year in matched elderly controls (MMSE 19.6±4.1 and 29.2±1.0 at baseline, for patients and controls, respectively). These measures are slightly lower than ours (5.22±2.04%/year in AD, 0.88±0.15%/year in controls), although our patients are slightly more severely impaired (MMSE falling from 18 to 13 at follow-up). This may support the idea that atrophic rates accelerate as the disease progresses (Kaye et al., 1999). A limitation of our current study is our use of only two time-points per subject, which forces us to assume linear loss during the interscan interval. Future

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studies using multiple time-points per patient (e.g. Janke et al., 2001) will reveal whether AD accelerates or progresses nonlinearly over time.

The profound deficits observed here further support the focus on temporal lobe as a site of early and progressive change in AD (Murphy et al., 1993; Kaye et al., 1997; Jack et al., 1998; Laakso et al., 2000). Jobst et al. (1994) noted faster change rates, even prior to symptom development, in normal individuals who went on to develop mild cognitive impairment (MCI). In future, cortical maps may help to map pre-clinical brain change in those at genetic risk for AD (e.g. ApoE4 carriers; Small et al., 2000; Reiman et al., 2000; cf. Thompson et al., 2002).

In summary, the dynamic maps presented here suggest that dynamic structural changes in AD, mapped in living patients, are congruent with earlier cross-sectional metabolic and pathologic changes. This sheds light on the complex pattern and timing of these cortical events. The overall strategy described here also provides quantitative and visual criteria to assess genetic effects on brain structure (Thompson et al., 2001), and to map drug effects in clinical trials.

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Acknowledgments

This work was supported by research grants from the National Center for Research Resources (P41 RR13642 and RR00865), the National Library of Medicine (LM/MH05639), National Institute of Neurological Disorders and Stroke and the National Institute of Mental Health (NINDS/NIMH NS38753 and MH65166), GlaxoSmithKline Pharmaceuticals UK, and by a Human Brain Project grant to the International Consortium for Brain Mapping, funded jointly by NIMH and NIDA (P20 MH/DA52176).

Figure Legends:

Figure 1. Creating 3D Average Cortical Models and Maps in AD and Elderly Populations: Cortical https://www.sodocs.net/doc/129549049.html,ing a cortical flattening process (a-f), and sulcal matching techniques (Figure 2), an average model of the cortex (Fig. 2(f)) can be built for a group of subjects. The goal of this process is to allow data (such as gray matter volumes) to be averaged from corresponding regions of cortex across subjects, reinforcing features that occur consistently. Briefly, the individual MRI scan (gray colors, panel (a)) is processed to split it up into gray matter (shown in green), white matter (shown in red), and cerebrospinal fluid (blue). A 3D cortical surface model (a) is extracted from the scan, and the following sulci are traced as 3D curves directly on this surface model [(b),(c)]: the superior and inferior frontal (SFS, IFS), pre- and postcentral (preCENT, poCENT), central (CENT), intraparietal (IP),

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superior temporal (STS), Sylvian fissures (SF), paracentral (paCENT), cingulate (CING) and paracingulate (paCING), subparietal (subP), callosal (CC), superior and inferior rostral (SRS, IRS), parieto-occipital (PAOC), anterior and posterior calcarine (CALCa/p) sulci. Because the surface is made up of discrete triangular tiles (c), a process of geometrical flattening can be applied to layout the cortical regions, and the sulcal curves that delimit them, as features in 2D (e). Information on where these cortical points originally came from in 3D can still be saved in this 2D image format. Using a color coding system, cortical point 3D locations (x,y,z) are given unique colors (with intensities of red, green and blue proportional to x,y, and z respectively), and these colors are plotted into the flat map. These color images represent the cortical shape and are used in Figure 2 to compute information on cortical pattern differences across subjects, and to make an ‘average shape’ cortex for a group of subjects.

Figure 2. Creating 3D Average Cortical Models and Maps in AD and Elderly Populations: Sulcal Matching. The idea behind sulcal matching is to average cortical data from corresponding regions across subjects, accommodating sulcal pattern differences across subjects using an elastic warping process. Briefly, the sulcal curves from all the subjects in the study are flattened and their shapes are averaged across subjects to make an average set of sulcal curves [shown in (a)]. The sulcal pattern of each individual, as seen in their flattened cortical map (a), differs a little from this average set of curves. A 2D elastic deformation can be applied to an individ ual’s flat map which drives its features into exact correspondence with the average set of sulcal curves (b). This same deformation can be applied to the color-coded image (c) that stores 3D cortical positions from that individual (see Fig. 1 for an explanation). Images such as (c) or (d) can be averaged, pixel-by-pixel, across all subjects in a group, and then decoded to produce a 3D shape. If this is done before sulcal matching, on images such as (c), a smooth cortex results (e). Intriguingly, if it is done on warped color images suchas (d), a crisp average cortex results, which group features reinforced in their mean anatomic locations. This process can create average cortical models for a group of subjects, but it can also transfer cortical data (such as gray matter density information) from many subjects onto a common cortical surface for comparison. In doing so, it accommodates complex differences in cortical patterning across subjects.

Figure 3. Image Processing Steps applied to a Individual Scans in this Study. This flow chart illustrates the key steps used to process the MRI brain scans in this study. They are illustrated here on example brain MRI datasets from a healthy control subject (left column) and from a patient with Alzheimer’s disease (right column). First, the MRI images (stage 1) have extracerebral tissues deleted from the scans and the individual pixels are classified as gray matter, white matter or CSF (shown here in green, red, and blue colors; stage 2). After flattening a 3D geometric model of the cortex (stage 3), features such as the central sulcus (light blue curve), and cingulate sulcus (green curve) may be reidentified. An elastic warp is applied (stage 4) moving these features, and entire gyral regions (pink colors), into the same reference position in flat space. After aligning sulcal patterns from all individual subjects, group comparisons can be made at each 2D pixel (yellow cross-hairs) that effectively compare gray matter measures across corresponding cortical regions. In this study, the cortical measure that is compared, across groups, and over time, is the amount of gray matter (stage 2) lying within 15 mm of each cortical point. The results of these statistical tests can then be plotted back onto an

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