Publications
Text queries can be conducted by Author, Title, or Keyword.
DYNAMIC MAPPING OF ALZHEIMER’S DISEASE
Source: 2004 Dec;.
Author: Thompson PM, Hayashi KM, de Zubicaray G, Janke AL, Sowell ER, Rose SE, Semple J, Herman D, Hong MS, Dittmer SS, Doddrell DM, Toga AW.
Abstract:
Neuroimaging strategies to track Alzheimer’s disease are greatly accelerating our understanding of the disease. How early can we detect disease-related brain changes? How do these changes progress anatomically? Do drugs slow down the physical spread of the disease? Brain imaging now provides answers to some of these important questions. With recent innovations in magnetic resonance imaging (MRI) and brain image analysis, Alzheimer’s disease can be mapped dynamically as it spreads in the living brain (Reiman et al., 2001; Fox et al., 2001; Janke et al., 2001; Thompson et al., 2003). Drug and gene effects on the disease process can be detected, both in patients and in family members at increased genetic risk. We show how these brain mapping tools help explore the dynamic processes of aging and dementia, revealing factors that affect them. As an illustrative example, we report the mapping of a dynamically spreading wave of gray matter loss in the brains of Alzheimer’s patients, scanned repeatedly with MRI. The loss pattern is visualized, in 3D, as it spreads from temporal cortices into frontal and cingulate brain regions. Deficit patterns are resolved with a novel cortical pattern matching strategy (CPM). A dynamic mapping technique produces color-coded image sequences that reveal the disease spreading in the human cortex over a period of several years. The trajectory of cortical deficits, observed here in vivo with MRI, corresponded closely to the spread of the underlying pathology (as defined by the wellknown Braak stages of neurofibrillary tangle and beta-amyloid accumulation). The magnitude of these deficits was also tightly linked with cognitive decline. In initial studies, these maps detected disease effects more sensitively than conventional cortical anatomic volume measures. By storing these dynamic brain maps in a growing, population-based digital atlas (N>1000 subjects), clinical imaging data can be analyzed on a large scale, adjusting for effects of age, sex, genotype, and disease subtypes. These maps chart the dynamic progress of Alzheimer’s disease and reveal a changing pattern of cortical deficits. We are now using them to detect where deficit patterns are modified by drug treatment and known risk genotypes.
Source: 2004 Dec;.
Author: Thompson PM, Hayashi KM, de Zubicaray G, Janke AL, Sowell ER, Rose SE, Semple J, Herman D, Hong MS, Dittmer SS, Doddrell DM, Toga AW.
Abstract:
Neuroimaging strategies to track Alzheimer’s disease are greatly accelerating our understanding of the disease. How early can we detect disease-related brain changes? How do these changes progress anatomically? Do drugs slow down the physical spread of the disease? Brain imaging now provides answers to some of these important questions. With recent innovations in magnetic resonance imaging (MRI) and brain image analysis, Alzheimer’s disease can be mapped dynamically as it spreads in the living brain (Reiman et al., 2001; Fox et al., 2001; Janke et al., 2001; Thompson et al., 2003). Drug and gene effects on the disease process can be detected, both in patients and in family members at increased genetic risk. We show how these brain mapping tools help explore the dynamic processes of aging and dementia, revealing factors that affect them. As an illustrative example, we report the mapping of a dynamically spreading wave of gray matter loss in the brains of Alzheimer’s patients, scanned repeatedly with MRI. The loss pattern is visualized, in 3D, as it spreads from temporal cortices into frontal and cingulate brain regions. Deficit patterns are resolved with a novel cortical pattern matching strategy (CPM). A dynamic mapping technique produces color-coded image sequences that reveal the disease spreading in the human cortex over a period of several years. The trajectory of cortical deficits, observed here in vivo with MRI, corresponded closely to the spread of the underlying pathology (as defined by the wellknown Braak stages of neurofibrillary tangle and beta-amyloid accumulation). The magnitude of these deficits was also tightly linked with cognitive decline. In initial studies, these maps detected disease effects more sensitively than conventional cortical anatomic volume measures. By storing these dynamic brain maps in a growing, population-based digital atlas (N>1000 subjects), clinical imaging data can be analyzed on a large scale, adjusting for effects of age, sex, genotype, and disease subtypes. These maps chart the dynamic progress of Alzheimer’s disease and reveal a changing pattern of cortical deficits. We are now using them to detect where deficit patterns are modified by drug treatment and known risk genotypes.