Dynamics of Gray Matter Loss in Alzheimer's Disease

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 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.

Brain Mapping as a Tool to Study Neurodegeneration

“…The cortical pattern-matching technique 70 is an advanced computational anatomy imaging methodology that uses sulcal constraints while elastically deforming each subject's cortical morphology into the group average representation. Cortical surface models, derived from structural MRI data, may be used to display a wide variety of imaging data, such as cortical thickness, gray matter density, functional activation data, or metabolic data from PET scans.…”

“…70 The baseline gray matter density comparison 17 compared the baseline hippocampal radial atrophy of MCI subjects who converted to AD and MCI subjects who remained stable or improved cognitively during the 3 years of clinical follow-up. The top row shows a schematic representation of the hippocampal subfields based on two established sources.…”

“…78 Strong associations between MMSE and cortical atrophy were seen in the entorhinal, parahippocampal, precuneus, superior parietal, and subgenual cingulate/orbitofrontal cortices. 70,75 Impaired picture naming and semantic fluency correlated with atrophy in perisylvian cortical areas thought to house lexical phonologic and semantic representations and process the receptive and articulatory aspects of language. 77 Delayed recall performance was associated with bilateral precuneal, entorhinal, parahippocampal, inferior temporal, and left temporo-occipital cortical atrophy.…”

Amyloid-β imaging with PET in Alzheimer’s disease: is it feasible with current radiotracers and technologies?

“…Magnetic resonance imaging (MRI) scans of AD patients reveal the greatest degree of atrophy in the temporal and parietal lobes, with frontal lobe damage delayed until the late stages of the disease [18][19][20]. Likewise, functional MRI studies have shown that the most pronounced differences in blood flow between AD patients and controls occur in the temporal and parietal lobes [21].…”

Soliton dynamics in computational anatomy

“…Following the pioneering work by Bookstein, Grenander and Bajscy [4], [8], [22], the past several years have seen an explosion in the use of template matching methods in computer vision and medical imaging [3], [14], [24], [25], [30], [33], [36]- [40], [51], [53]- [56]. These methods have enabled the systematic measurement and comparison of anatomical shapes and structures in biomedical imagery leading to better understanding of neurodevelopmental, neuropsychiatric and neurological disorders in recent years [5], [6], [11], [13], [17], [20], [42]- [48], [50], [52], [59]. The mathematical theory of Grenander's deformable template models, when applied to these problems, involves smooth invertible maps (diffeomorphisms), as presented in this context in [55], [56], [14], [41], [36] and [33].…”