Literature DB >> 12849264

Computer-assisted imaging to assess brain structure in healthy and diseased brains.

John Ashburner1, John G Csernansky, Christos Davatzikos, Nick C Fox, Giovanni B Frisoni, Paul M Thompson.   

Abstract

Neuroanatomical structures may be profoundly or subtly affected by the interplay of genetic and environmental factors, age, and disease. Such effects are particularly true in healthy ageing individuals and in those who have neurodegenerative diseases. The ability to use imaging to identify structural brain changes associated with different neurodegenerative disease states would be useful for diagnosis and treatment. However, early in the progression of such diseases, neuroanatomical changes may be too mild, diffuse, or topologically complex to be detected by simple visual inspection or manually traced measurements of regions of interest. Computerised methods are being developed that can capture the extraordinary morphological variability of the human brain. These methods use mathematical models sensitive to subtle changes in the size, position, shape, and tissue characteristics of brain structures affected by neurodegenerative diseases. Neuroanatomical features can be compared within and between groups of individuals, taking into account age, sex, genetic background, and disease state, to assess the structural basis of normality and disease. In this review, we describe the strengths and limitations of algorithms of existing computer-assisted tools at the most advanced stage of development, together with available and foreseeable evidence of their usefulness at the clinical and research level.

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Mesh:

Year:  2003        PMID: 12849264     DOI: 10.1016/s1474-4422(03)00304-1

Source DB:  PubMed          Journal:  Lancet Neurol        ISSN: 1474-4422            Impact factor:   44.182


  109 in total

1.  Progressive gray matter loss and changes in cognitive functioning associated with exposure to herpes simplex virus 1 in schizophrenia: a longitudinal study.

Authors:  Konasale M Prasad; Shaun M Eack; Dhruman Goradia; Krishna M Pancholi; Matcheri S Keshavan; Robert H Yolken; Vishwajit L Nimgaonkar
Journal:  Am J Psychiatry       Date:  2011-06-01       Impact factor: 18.112

Review 2.  Quantitative structural MRI for early detection of Alzheimer's disease.

Authors:  Linda K McEvoy; James B Brewer
Journal:  Expert Rev Neurother       Date:  2010-11       Impact factor: 4.618

Review 3.  Computational analysis of cerebral cortex.

Authors:  Hidemasa Takao; Osamu Abe; Kuni Ohtomo
Journal:  Neuroradiology       Date:  2010-05-18       Impact factor: 2.804

4.  Brainstem morphological changes in Alzheimer's disease.

Authors:  Ji Han Lee; John Ryan; Carmen Andreescu; Howard Aizenstein; Hyun Kook Lim
Journal:  Neuroreport       Date:  2015-05-06       Impact factor: 1.837

Review 5.  Multiple sclerosis and Alzheimer disease through the looking glass of MR imaging.

Authors:  Giovanni B Frisoni; Massimo Filippi
Journal:  AJNR Am J Neuroradiol       Date:  2005 Nov-Dec       Impact factor: 3.825

Review 6.  Differential aging of the brain: patterns, cognitive correlates and modifiers.

Authors:  Naftali Raz; Karen M Rodrigue
Journal:  Neurosci Biobehav Rev       Date:  2006-08-17       Impact factor: 8.989

7.  Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils.

Authors:  Richard G Boyes; Jeff L Gunter; Chris Frost; Andrew L Janke; Thomas Yeatman; Derek L G Hill; Matt A Bernstein; Paul M Thompson; Michael W Weiner; Norbert Schuff; Gene E Alexander; Ronald J Killiany; Charles DeCarli; Clifford R Jack; Nick C Fox
Journal:  Neuroimage       Date:  2007-10-30       Impact factor: 6.556

Review 8.  Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function.

Authors:  David C Glahn; Paul M Thompson; John Blangero
Journal:  Hum Brain Mapp       Date:  2007-06       Impact factor: 5.038

Review 9.  Brain glucose metabolism in the early and specific diagnosis of Alzheimer's disease. FDG-PET studies in MCI and AD.

Authors:  Lisa Mosconi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2005-04       Impact factor: 9.236

10.  H1 haplotype of the MAPT gene is associated with lower regional gray matter volume in healthy carriers.

Authors:  Elisa Canu; Marina Boccardi; Roberta Ghidoni; Luisa Benussi; Cristina Testa; Michela Pievani; Matteo Bonetti; Giuliano Binetti; Giovanni B Frisoni
Journal:  Eur J Hum Genet       Date:  2008-10-15       Impact factor: 4.246

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