ADNI Publications

Journal Articles



1. Leow AD, Klunder AD, Jack CR, Toga AW, Dale AM, Bernstein MA, Britson PJ, Gunter JL, Ward CP, Whitwell JL, Borowski B, Fleisher A, Fox NC, Harvey D, Kornak J, Schuff N, Studholme C, Alexander GE, Weiner MW, Thompson PM*, For the ADNI Preparatory Phase Study (2006). Longitudinal Stability of MRI for Mapping Brain Change using Tensor-Based Morphometry, NeuroImage, 2006 Feb 7; [Epub ahead of print] [*corresponding author]. [569KB, .pdf]

   This paper examines the longitudinal stability of different types of MRI scans for tracking brain change over time.

2. Hua X, Leow AD, Lee S, Klunder AD, Toga AW, Lepore N, Chou YY, Brun C, Chiang MC, Barysheva M, Jack CR, Bernstein MA, Britson PJ, Ward CP, Whitwell JL, Borowski B, Fleisher A, Fox NC, Boyes R, Barnes J, Harvey D, Kornak J, Schuff N, Boreta L, Alexander GE, Weiner MW, Thompson PM*, For the ADNI Study (2008). 3D Characterization of Brain Atrophy in Alzheimer's Disease and Mild Cognitive Impairment using Tensor-based Morphometry, NeuroImage, published online, Feb. 21 2008. [2.8MB, .pdf]

   This paper shows how a method called tensor based morphometry can be optimized to map the 3D profile of brain degeneration.

3. Hua X, Leow AD, Parikshak N, Lee S, Chiang MC, Toga AW, Jack CR, Weiner MW, Thompson PM*, For the ADNI Study (2008). Tensor-Based Morphometry as a Neuroimaging Biomarker for Alzheimer's Disease: An MRI Study of 676 AD, MCI, and Normal Subjects, NeuroImage, in press, July 2008. [1.9MB, .pdf] [NEW]

   This paper reveals how the 3D profile of brain deficits in 676 people correlates with genetic measures, cognitive decline, and future outcomes.

   

4. Morra J, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Hua X, Toga AW, Jack CR, Schuff N, Weiner MW, Thompson PM (2008). Validation of a Fully Automated 3D Hippocampal Segmentation Method Using Subjects with Alzheimer's Disease, Mild Cognitive Impairment, and Elderly Controls, NeuroImage, published online, July 20 2008. [0.9MB, .pdf] [NEW]

   Here we validate a novel method that can automatically map hippocampal degeneration in hundreds of subjects, using a brain image database.

5. Morra J, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Hua X, Toga AW, Jack CR, Schuff N, Weiner MW, Thompson PM (2008). Automated 3D Mapping of Hippocampal Atrophy and its Clinical Correlates in 400 Subjects with Alzheimer's Disease, Mild Cognitive Impairment, and Elderly Controls, Proceedings of the ISBI 2008, Paris, France. [2MB, .pdf]

   This is a very short paper summarizing how the same automated method visualizes clinical correlates of hippocampal atrophy in 400 subjects.

6. Morra J, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Hua X, Toga AW, Jack CR, Schuff N, Weiner MW, Thompson PM (2008). Automated 3D Mapping of Hippocampal Atrophy and its Clinical Correlates in 400 Subjects with Alzheimer's Disease, Mild Cognitive Impairment, and Elderly Controls, Human Brain Mapping, Dec. 2008. [5.6MB, .pdf]

   An expanded version of the prior paper examines clinical correlates of hippocampal atrophy in 400 subjects, including depression, blood pressure, homocysteine levels, education, and ApoE genotype (ApoE4 is a risk gene for Alzheimer's disease).

7. Morra J, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Toga AW, Jack CR, Schuff N, Weiner MW, Thompson PM (2008). Automated Mapping of Hippocampal Atrophy in 1-Year Repeat MRI Data in 490 Subjects with Alzheimer's Disease, Mild Cognitive Impairment, and Elderly Controls, NeuroImage, Special Issue on Mathematics in Brain Imaging, ed. Thompson PM, Miller MI, Poldrack R, Nichols T, et al., published online, Nov. 8 2008.

   Here we identify factors that affect hippocampal degeneration over time in 490 patients scanned twice, with 1 year between the scans (i.e., 980 scans total).

8. Morra J, Tu Z, Toga AW, Thompson PM (2009). Machine Learning for Brain Image Segmentation, Tutorial Chapter in Textbook: Biomedical Image Analysis and Machine Learning Technologies, ed. Gonzalez F, Romero E, to appear: 2009. [0.8MB, .pdf]

   Here we explain how machine learning techniques, such as adaptive boosting, can be used to analyze brain degeneration automatically in thousands of images.