William Jagust1. 1. School of Public Health and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA. jagust@berkeley.edu
Abstract
BACKGROUND: The diagnosis of dementia, along with the prediction of who will develop dementia, has been assisted by the development of the brain imaging techniques of magnetic resonance imaging (MRI) and positron emission tomography (PET). METHODS: This paper reviews the brain imaging technologies of structural MRI and PET scanning as they have been applied to both the diagnosis of dementia and prediction of who will develop dementia. RESULTS: Diagnosis has long been enhanced by the use of structural imaging techniques like MRI to rule out non-degenerative causes of disease. More recently, PET imaging with the glucose metabolic tracer [(18)F]Fluorodeoxyglucose (FDG) may be useful in providing information on the cause of dementia during life, most specifically in differentiating Alzheimer's disease from frontotemporal lobar degeneration. In addition to diagnosis, potential therapeutic advances have increased interest in prediction of dementia. Both MR and FDG-PET have shown evidence of change in brain structure and metabolism in several models of individuals at-risk for dementia, including those with mild cognitive impairment and genetic risk factors. CONCLUSIONS: While these studies have not yet advanced to the level of prospective individual-subject predictive ability, the pattern of data emerging suggests likely candidate approaches for such studies. The advent of newer techniques such as amyloid imaging with PET and functional MRI may ultimately have relevance for both diagnosis and prediction.
BACKGROUND: The diagnosis of dementia, along with the prediction of who will develop dementia, has been assisted by the development of the brain imaging techniques of magnetic resonance imaging (MRI) and positron emission tomography (PET). METHODS: This paper reviews the brain imaging technologies of structural MRI and PET scanning as they have been applied to both the diagnosis of dementia and prediction of who will develop dementia. RESULTS: Diagnosis has long been enhanced by the use of structural imaging techniques like MRI to rule out non-degenerative causes of disease. More recently, PET imaging with the glucose metabolic tracer [(18)F]Fluorodeoxyglucose (FDG) may be useful in providing information on the cause of dementia during life, most specifically in differentiating Alzheimer's disease from frontotemporal lobar degeneration. In addition to diagnosis, potential therapeutic advances have increased interest in prediction of dementia. Both MR and FDG-PET have shown evidence of change in brain structure and metabolism in several models of individuals at-risk for dementia, including those with mild cognitive impairment and genetic risk factors. CONCLUSIONS: While these studies have not yet advanced to the level of prospective individual-subject predictive ability, the pattern of data emerging suggests likely candidate approaches for such studies. The advent of newer techniques such as amyloid imaging with PET and functional MRI may ultimately have relevance for both diagnosis and prediction.
Authors: Marilyn S Albert; Steven T DeKosky; Dennis Dickson; Bruno Dubois; Howard H Feldman; Nick C Fox; Anthony Gamst; David M Holtzman; William J Jagust; Ronald C Petersen; Peter J Snyder; Maria C Carrillo; Bill Thies; Creighton H Phelps Journal: Alzheimers Dement Date: 2011-04-21 Impact factor: 21.566
Authors: Jaqueline Hatsuko Tamashiro-Duran; Paula Squarzoni; Fábio Luís de Souza Duran; Pedro Kallas Curiati; Homero Pinto Vallada; Carlos Alberto Buchpiguel; Paulo Andrade Lotufo; Mauricio Wajngarten; Paulo Rossi Menezes; Márcia Scazufca; Tânia Corrêa de Toledo Ferraz Alves; Geraldo Filho Busatto Journal: Age (Dordr) Date: 2012-04-29
Authors: Jane Maryam Rondina; Paula Squarzoni; Fabio Luis Souza-Duran; Jaqueline Hatsuko Tamashiro-Duran; Marcia Scazufca; Paulo Rossi Menezes; Homero Vallada; Paulo A Lotufo; Tania Correa de Toledo Ferraz Alves; Geraldo Busatto Filho Journal: Front Aging Neurosci Date: 2014-12-01 Impact factor: 5.750