Neuroimaging Alzheimer’s Disease

Abstract

Imaging studies of clinical populations continue to uncover new patterns of altered structure and function, and novel algorithms are being applied to relate these patterns to cognitive and genetic parameters. Post mortem brain maps are also beginning to clarify the molecular substrates of disease. As imaging studies expand into ever-larger patient populations, population-based brain atlases (Mazziotta et al., 1995; Thompson, Mega and Toga, 2000) offer a powerful framework to synthesize results from disparate imaging studies. These atlases use novel analytical tools to fuse data across subjects, modalities, and time. They detect group-specific features not apparent in individual patients' scans. Once built, these atlases can be stratified into subpopulations to reflect a particular clinical group, such as individuals at genetic risk for AD, patients with mild cognitive impairment (MCI) or different dementia subtypes (frontotemporal dementia/semantic dementia), or patients undergoing different drug treatments. The disease-specific features these atlases resolve can then be linked with demographic factors such as age, gender, handedness, as well as specific clinical or genetic parameters (Mazziotta et al. New brain atlases are also being built to incorporate dynamic data (Thompson et al., 2002). Despite the significant challenges in expanding the atlas concept to the time dimension, dynamic brain atlases are beginning to include probabilistic information on growth rates that may assist research into pediatric disorders (Thompson et al., 2000) as well as revealing patterns of degenerative rates in Alzheimer's disease Imaging algorithms are also significantly improving the flexibility of digital brain templates. Deformable brain atlases are adaptable brain templates that can be individualized to reflect the anatomy of new subjects. These atlases may be used for automated parcellation of new brain scans (Collins et al., 1995; Iosifescu et al., 1997), to define regions of interest in functional and metabolic studies (Dinov et al, 2000), and for anatomical shape assessment (Thompson et al., 1997; Csernansky et al., 2000). Probabilistic atlases are research tools that retain information on cross-subject variations in brain structure and function. These atlases are powerful new tools with broad clinical and research applications Disease-Specific Atlases. This chapter introduces the topic of a disease-specific brain atlas (Fig. 1). This type of atlas is designed to reflect the unique anatomy and physiology of a particular clinical Based on well-characterized patient groups, these atlases contain thousands of structure models, as well as composite maps, average templates, and visualizations of structural variability, asymmetry and group-specific differences. They act as a …

Cite this paper

@inproceedings{Esiri2002NeuroimagingAD, title={Neuroimaging Alzheimer’s Disease}, author={Margaret M. Esiri and James H. Morris and Arthur W. Toga}, year={2002} }