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Brain region's relative proximity as marker for Alzheimer's disease based on structural MRI.

Lillemark L, Sørensen L, Pai A, Dam EB, Nielsen M, Alzheimer's Disease Neuroimaging Initiati - BMC Med Imaging (2014)

Bottom Line: We found that both our markers was able to significantly classify the subjects.The surface connectivity marker showed the best results with an area under the curve (AUC) at 0.877 (p<0.001), 0.784 (p<0.001), 0,766 (p<0.001) for NC-AD, NC-MCI, and MCI-AD, respectively, for the functional regions in the brain.Our results demonstrate that our proximity markers have the potential to assist in early diagnosis of AD.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark. lene.lillemark@gmail.com.

ABSTRACT

Background: Alzheimer's disease (AD) is a progressive, incurable neurodegenerative disease and the most common type of dementia. It cannot be prevented, cured or drastically slowed, even though AD research has increased in the past 5-10 years. Instead of focusing on the brain volume or on the single brain structures like hippocampus, this paper investigates the relationship and proximity between regions in the brain and uses this information as a novel way of classifying normal control (NC), mild cognitive impaired (MCI), and AD subjects.

Methods: A longitudinal cohort of 528 subjects (170 NC, 240 MCI, and 114 AD) from ADNI at baseline and month 12 was studied. We investigated a marker based on Procrustes aligned center of masses and the percentile surface connectivity between regions. These markers were classified using a linear discriminant analysis in a cross validation setting and compared to whole brain and hippocampus volume.

Results: We found that both our markers was able to significantly classify the subjects. The surface connectivity marker showed the best results with an area under the curve (AUC) at 0.877 (p<0.001), 0.784 (p<0.001), 0,766 (p<0.001) for NC-AD, NC-MCI, and MCI-AD, respectively, for the functional regions in the brain. The surface connectivity marker was able to classify MCI-converters with an AUC of 0.599 (p<0.05) for the 1-year period.

Conclusion: Our results show that our relative proximity markers include more information than whole brain and hippocampus volume. Our results demonstrate that our proximity markers have the potential to assist in early diagnosis of AD.

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Related in: MedlinePlus

(a) show the ROC for AD vs NC, (b) shows the ROC for NC vs. MCI, and (c) shows the ROC for MCI vs. AD.
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Figure 3: (a) show the ROC for AD vs NC, (b) shows the ROC for NC vs. MCI, and (c) shows the ROC for MCI vs. AD.

Mentions: We have also investigated how our markers performed on the period to month 12 using the score differences between bl and month 12 for each marker, and the AUC and the corresponding ranksum p-values are shown in Table 6 and roc curves in Figure 3. Hippocampus and whole brain showed relatively low AUC result due to the use of static Freesurfer volumes from bl and month 12. Our surface connectivity scores performed the best for all three groups NC-AD, NC-MCI, and MCI-AD. The results between NC-AD and NC-MCI are very similar.


Brain region's relative proximity as marker for Alzheimer's disease based on structural MRI.

Lillemark L, Sørensen L, Pai A, Dam EB, Nielsen M, Alzheimer's Disease Neuroimaging Initiati - BMC Med Imaging (2014)

(a) show the ROC for AD vs NC, (b) shows the ROC for NC vs. MCI, and (c) shows the ROC for MCI vs. AD.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4048460&req=5

Figure 3: (a) show the ROC for AD vs NC, (b) shows the ROC for NC vs. MCI, and (c) shows the ROC for MCI vs. AD.
Mentions: We have also investigated how our markers performed on the period to month 12 using the score differences between bl and month 12 for each marker, and the AUC and the corresponding ranksum p-values are shown in Table 6 and roc curves in Figure 3. Hippocampus and whole brain showed relatively low AUC result due to the use of static Freesurfer volumes from bl and month 12. Our surface connectivity scores performed the best for all three groups NC-AD, NC-MCI, and MCI-AD. The results between NC-AD and NC-MCI are very similar.

Bottom Line: We found that both our markers was able to significantly classify the subjects.The surface connectivity marker showed the best results with an area under the curve (AUC) at 0.877 (p<0.001), 0.784 (p<0.001), 0,766 (p<0.001) for NC-AD, NC-MCI, and MCI-AD, respectively, for the functional regions in the brain.Our results demonstrate that our proximity markers have the potential to assist in early diagnosis of AD.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark. lene.lillemark@gmail.com.

ABSTRACT

Background: Alzheimer's disease (AD) is a progressive, incurable neurodegenerative disease and the most common type of dementia. It cannot be prevented, cured or drastically slowed, even though AD research has increased in the past 5-10 years. Instead of focusing on the brain volume or on the single brain structures like hippocampus, this paper investigates the relationship and proximity between regions in the brain and uses this information as a novel way of classifying normal control (NC), mild cognitive impaired (MCI), and AD subjects.

Methods: A longitudinal cohort of 528 subjects (170 NC, 240 MCI, and 114 AD) from ADNI at baseline and month 12 was studied. We investigated a marker based on Procrustes aligned center of masses and the percentile surface connectivity between regions. These markers were classified using a linear discriminant analysis in a cross validation setting and compared to whole brain and hippocampus volume.

Results: We found that both our markers was able to significantly classify the subjects. The surface connectivity marker showed the best results with an area under the curve (AUC) at 0.877 (p<0.001), 0.784 (p<0.001), 0,766 (p<0.001) for NC-AD, NC-MCI, and MCI-AD, respectively, for the functional regions in the brain. The surface connectivity marker was able to classify MCI-converters with an AUC of 0.599 (p<0.05) for the 1-year period.

Conclusion: Our results show that our relative proximity markers include more information than whole brain and hippocampus volume. Our results demonstrate that our proximity markers have the potential to assist in early diagnosis of AD.

Show MeSH
Related in: MedlinePlus