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Link to original content: https://pubmed.ncbi.nlm.nih.gov/21904533/
Multi-modal MRI analysis with disease-specific spatial filtering: initial testing to predict mild cognitive impairment patients who convert to Alzheimer's disease - PubMed Skip to main page content
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. 2011 Aug 24:2:54.
doi: 10.3389/fneur.2011.00054. eCollection 2011.

Multi-modal MRI analysis with disease-specific spatial filtering: initial testing to predict mild cognitive impairment patients who convert to Alzheimer's disease

Kenichi Oishi  1 , Kazi Akhter, Michelle Mielke, Can Ceritoglu, Jiangyang Zhang, Hangyi Jiang, Xin Li, Laurent Younes, Michael I Miller, Peter C M van Zijl, Marilyn Albert, Constantine G Lyketsos, Susumu Mori
Affiliations

Multi-modal MRI analysis with disease-specific spatial filtering: initial testing to predict mild cognitive impairment patients who convert to Alzheimer's disease

Kenichi Oishi et al. Front Neurol. .

Abstract

Background: Alterations of the gray and white matter have been identified in Alzheimer's disease (AD) by structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). However, whether the combination of these modalities could increase the diagnostic performance is unknown.

Methods: Participants included 19 AD patients, 22 amnestic mild cognitive impairment (aMCI) patients, and 22 cognitively normal elderly (NC). The aMCI group was further divided into an "aMCI-converter" group (converted to AD dementia within 3 years), and an "aMCI-stable" group who did not convert in this time period. A T(1)-weighted image, a T(2) map, and a DTI of each participant were normalized, and voxel-based comparisons between AD and NC groups were performed. Regions-of-interest, which defined the areas with significant differences between AD and NC, were created for each modality and named "disease-specific spatial filters" (DSF). Linear discriminant analysis was used to optimize the combination of multiple MRI measurements extracted by DSF to effectively differentiate AD from NC. The resultant DSF and the discriminant function were applied to the aMCI group to investigate the power to differentiate the aMCI-converters from the aMCI-stable patients.

Results: The multi-modal approach with AD-specific filters led to a predictive model with an area under the receiver operating characteristic curve (AUC) of 0.93, in differentiating aMCI-converters from aMCI-stable patients. This AUC was better than that of a single-contrast-based approach, such as T(1)-based morphometry or diffusion anisotropy analysis.

Conclusion: The multi-modal approach has the potential to increase the value of MRI in predicting conversion from aMCI to AD.

Keywords: Alzheimer’s disease; diffusion tensor imaging; magnetic resonance imaging; mild cognitive impairment; multi-modal disease-specific spatial filtering; pre-dementia phase; white matter.

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Figures

Figure 1
Figure 1
Flow chart of the study.
Figure 2
Figure 2
Voxel-based group comparison between AD and NC. Areas with signal or volume alterations in AD compared to NC are shown as colored maps, overlaid on an averaged FA map (A–E), an averaged T2 map (F), and an averaged GM segmentation map (G), created from all 63 images. (A): Areas with reduced FA. (B) Areas with increased MD. (C) Areas with increased λ||. (D) Areas with increased λ⊥. (E) Areas with an increased and decreased Jacobian, which was calculated from a transformation matrix obtained from the normalization of DTI. (F) Area with increased T2. (G) Areas with increased and decreased Jacobian, which were calculated from a transformation matrix obtained from the normalization of a GM segmentation map. White arrows show the misregistration seen in the left posterior horn of the lateral ventricle.
Figure 3
Figure 3
Top and left-side view of the eight disease-specific spatial filters (DSFs) created from voxel-based statistical comparisons of the training dataset (AD and NC). The brain surface and the hippocampal surface of the JHU-MNI atlas are shown in gray and pink, respectively.
Figure 4
Figure 4
Scattergrams of the measured DSF values of the eight parameters, the discriminant scores (DTI score and MRI score), and the results of cognitive tests of the test dataset (MCI-converter and MCI-stable). MD, λ||, and λ⊥: 10−3 × mm2/s; T2: ms. C: aMCI-converter; S: aMCI-stable.
Figure 5
Figure 5
Results of the receiver operating characteristic curve (ROC) analyses. (A) The ROCs of various MR measurements. (B) The ROCs of various cognitive tests. WMS-imm: number of correct answers in the immediate story recall of the WMS-III; WMS-del: number of correct answers in the delayed story recall of the WMS-III.
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