Volume 28, Issue 3 pp. 735-744

Original Article

Associations between texture of T1-weighted magnetic resonance imaging and radiographic pathologies in Alzheimer’s disease

Subin Lee,

Subin Lee

Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea

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Ki Woong Kim,

Corresponding Author

Ki Woong Kim

[email protected]

orcid.org/0000-0002-1103-3858

Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea

Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea

Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea

Correspondence

Ki Woong Kim, Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173 beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea.

Email: [email protected]

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for the Alzheimer’s Disease Neuroimaging Initiative,

for the Alzheimer’s Disease Neuroimaging Initiative

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf Search for more papers by this author

Subin Lee,

Subin Lee

Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea

Search for more papers by this author

Ki Woong Kim,

Corresponding Author

Ki Woong Kim

[email protected]

orcid.org/0000-0002-1103-3858

Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea

Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea

Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea

Correspondence

Ki Woong Kim, Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173 beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea.

Email: [email protected]

Search for more papers by this author

for the Alzheimer’s Disease Neuroimaging Initiative,

for the Alzheimer’s Disease Neuroimaging Initiative

First published: 24 October 2020

https://doi.org/10.1111/ene.14609

Citations: 12

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Abstract

Background and purpose

Texture analysis of magnetic resonance imaging (MRI) brain scans have been proposed as a promising tool in the early diagnosis of Alzheimer’s disease (AD), but its biological correlates remain unknown. In this study, we examined the relationship between MRI texture features and AD pathology.

Methods

The study included 150 participants who had a 3.0T T1-weighted image, amyloid-β positron emission tomography (PET), and tau PET within 3 months of each other. In each of six brain regions (hippocampus, precuneus, and entorhinal, middle temporal, posterior cingulate and superior frontal cortices), linear regression analyses adjusting for age and sex was performed to examine the effects of regional amyloid-β and tau burden on regional texture features. We also compared neuroimaging measures based on pathological severity using ANOVA.

Results

In all regions, tau burden (p < 0.05), but not amyloid-β burden, were associated with a certain texture feature that varied with the region’s cytoarchitecture. Specifically, autocorrelation and cluster shade were associated with tau burden in allocortical and periallocortical regions, whereas entropy and contrast were associated with tau burden in neocortical regions. Mean signal intensity of each region did not show any associations with AD pathology. The values of the region-specific textures also varied across groups of varying pathological severity.

Conclusions

Our results suggest that textures of T1-weighted MRI reflect changes in the brain that are associated with regional tau burden and the local cytoarchitecture. This study provides insight into how MRI texture can be used for detection of microstructural changes in AD.

CONFLICTS OF INTEREST

The authors declare that there are no conflicts of interest associated with this work.

Open Research

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supporting Information

References

1de Oliveira MS, Balthazar ML, D'Abreu A, et al. MR imaging texture analysis of the corpus callosum and thalamus in amnestic mild cognitive impairment and mild Alzheimer disease. Am J Neuroradiol. 2011; 32: 60–66.
10.3174/ajnr.A2232
PubMed Web of Science® Google Scholar
2Xia H, Tong L, Zhou X, Zhang J, Zhou Z, Liu W. Texture Analysis and Volumetry of Hippocampus and Medial Temporal Lobe in Patients with Alzheimer's Disease. Paper presented at: 2012 International Conference on Biomedical Engineering and Biotechnology; 28–30 May 2012, 2012.
Google Scholar
3Chincarini A, Bosco P, Calvini P, et al. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease. NeuroImage. 2011; 58: 469–480.
10.1016/j.neuroimage.2011.05.083
PubMed Web of Science® Google Scholar
4Feng F, Wang P, Zhao K, et al. Radiomic features of hippocampal subregions in Alzheimer’s disease and amnestic mild cognitive impairment. Front Aging Neurosci. 2018; 10: 290.
10.3389/fnagi.2018.00290
PubMed Web of Science® Google Scholar
5Freeborough PA, Fox NC. MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease. IEEE Trans Med Imaging. 1998; 17: 475–478.
10.1109/42.712137
CAS PubMed Web of Science® Google Scholar
6Lee S, Lee H, Kim K. Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume. J Psychiatry Neurosci. 2019; 44: 1–8.
Google Scholar
7Luk CC, Ishaque A, Khan M, et al. Alzheimer's disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment. Alzheimers Dement (Amst). 2018; 10: 755–763.
10.1016/j.dadm.2018.09.002
PubMed Google Scholar
8Nanni L, Brahnam S, Salvatore C, Castiglioni I. Texture descriptors and voxels for the early diagnosis of Alzheimer’s disease. Artif Intell Med. 2019; 97: 19–26.
10.1016/j.artmed.2019.05.003
PubMed Web of Science® Google Scholar
9Sørensen L, Igel C, Hansen NL, et al. Early detection of Alzheimer's disease using mri hippocampal texture. Hum Brain Mapp. 2016; 37: 1148–1161.
10.1002/hbm.23091
PubMed Web of Science® Google Scholar
10Deoni SCL. Quantitative relaxometry of the brain. Top Magn Reson Imaging. 2010; 21: 101–113.
10.1097/RMR.0b013e31821e56d8
PubMed Google Scholar
11Eickhoff S, Walters NB, Schleicher A, et al. High-resolution MRI reflects myeloarchitecture and cytoarchitecture of human cerebral cortex. Hum Brain Mapp. 2005; 24: 206–215.
10.1002/hbm.20082
CAS PubMed Web of Science® Google Scholar
12Braak H, Alafuzoff I, Arzberger T, Kretzschmar H, Del Tredici K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol. 2006; 112: 389–404.
10.1007/s00401-006-0127-z
PubMed Web of Science® Google Scholar
13Mattsson N, Palmqvist S, Stomrud E, Vogel J, Hansson O. Staging β-amyloid pathology with amyloid positron emission tomography. JAMA Neurol. 2019; 76(11): 1319–1329.
10.1001/jamaneurol.2019.2214
PubMed Web of Science® Google Scholar
14Buch K, Kuno H, Qureshi MM, Li B, Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: a phantom model. J Appl Clin Med Phys. 2018; 19: 253–264.
10.1002/acm2.12482
PubMed Web of Science® Google Scholar
15Schöll M, Lockhart SN, Schonhaut DR, et al. PET imaging of tau deposition in the aging human brain. Neuron. 2016; 89: 971–982.
10.1016/j.neuron.2016.01.028
CAS PubMed Web of Science® Google Scholar
16Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging. 2004; 22: 81–91.
10.1016/j.mri.2003.09.001
CAS PubMed Web of Science® Google Scholar
17Westlye LT, Walhovd KB, Dale AM, et al. Differentiating maturational and aging-related changes of the cerebral cortex by use of thickness and signal intensity. NeuroImage. 2010; 52: 172–185.
10.1016/j.neuroimage.2010.03.056
PubMed Web of Science® Google Scholar
18Haralick RM, Shanmugam K, Dinstein Ih. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973; 6: 610–621.
10.1109/TSMC.1973.4309314
Google Scholar
19Ortiz A, Palacio AA, Górriz JM, Ramírez J, Salas-González D. Segmentation of brain MRI using SOM-FCM-based method and 3D statistical descriptors. Comput Math Methods Med. 2013; 2013: 1–12.
10.1155/2013/638563
Web of Science® Google Scholar
20Conners RW, Trivedi MM, Harlow CA. Segmentation of a high-resolution urban scene using texture operators. Comput Vis Gr Image Process. 1984; 25: 273–310.
10.1016/0734-189X(84)90197-X
Web of Science® Google Scholar
21Greve DN, Salat DH, Bowen SL, et al. Different partial volume correction methods lead to different conclusions: An 18F-FDG-PET study of aging. NeuroImage. 2016; 132: 334–343.
10.1016/j.neuroimage.2016.02.042
PubMed Web of Science® Google Scholar
22Colgan N, Ganeshan B, Harrison IF, et al. In vivo imaging of tau pathology using magnetic resonance imaging textural analysis. Front Neurosci. 2017; 11: 599.
10.3389/fnins.2017.00599
PubMed Web of Science® Google Scholar
23Bartzokis G. Alzheimer's disease as homeostatic responses to age-related myelin breakdown. Neurobiol Aging. 2011; 32: 1341–1371.
10.1016/j.neurobiolaging.2009.08.007
CAS PubMed Web of Science® Google Scholar
24Bulk M, Abdelmoula WM, Nabuurs RJA, et al. Postmortem MRI and histology demonstrate differential iron accumulation and cortical myelin organization in early- and late-onset Alzheimer's disease. Neurobiol Aging. 2018; 62: 231–242.
10.1016/j.neurobiolaging.2017.10.017
CAS PubMed Web of Science® Google Scholar
25Pelkmans W, Dicks E, Barkhof F, et al. Gray matter T1-w/T2-w ratios are higher in Alzheimer's disease. Hum Brain Mapp. 2019; 40: 3900–3909.
10.1002/hbm.24638
PubMed Web of Science® Google Scholar
26Yasuno F, Kazui H, Morita N, et al. Use of T1-weighted/T2-weighted magnetic resonance ratio to elucidate changes due to amyloid β accumulation in cognitively normal subjects. NeuroImage Clin. 2017; 13: 209–214.
10.1016/j.nicl.2016.11.029
PubMed Web of Science® Google Scholar
27Kenkhuis B, Jonkman LE, Bulk M, et al. 7T MRI allows detection of disturbed cortical lamination of the medial temporal lobe in patients with Alzheimer's disease. NeuroImage Clin. 2019; 21:101665.
10.1016/j.nicl.2019.101665
PubMed Web of Science® Google Scholar
28Leuze C, Aswendt M, Ferenczi E, et al. The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing. NeuroImage. 2017; 156: 412–422.
10.1016/j.neuroimage.2017.04.021
CAS PubMed Web of Science® Google Scholar
29Casanova R, Whitlow CT, Wagner B, et al. High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization. Front Neuroinform. 2011; 5: 22.
10.3389/fninf.2011.00022
PubMed Google Scholar
30Fan Y, Batmanghelich N, Clark CM, Davatzikos C. Initiative AsDN. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. NeuroImage. 2008; 39: 1731–1743.
10.1016/j.neuroimage.2007.10.031
PubMed Web of Science® Google Scholar
31Klöppel S, Stonnington CM, Chu C, et al. Automatic classification of MR scans in Alzheimer's disease. Brain. 2008; 131: 681–689.
10.1093/brain/awm319
PubMed Web of Science® Google Scholar
32Vemuri P, Whitwell JL, Kantarci K, et al. Antemortem MRI based STructural Abnormality iNDex (STAND)-scores correlate with postmortem Braak neurofibrillary tangle stage. NeuroImage. 2008; 42: 559–567.
10.1016/j.neuroimage.2008.05.012
PubMed Web of Science® Google Scholar
33Arnold SE, Hyman BT, Flory J, Damasio AR, Van Hoesen GW. The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease. Cereb Cortex. 1991; 1: 103–116.
10.1093/cercor/1.1.103
CAS PubMed Web of Science® Google Scholar
34Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991; 82: 239–259.
10.1007/BF00308809
CAS PubMed Web of Science® Google Scholar
35Hyman BT, Van Hoesen GW, Damasio AR, Barnes CL. Alzheimer's disease: cell-specific pathology isolates the hippocampal formation. Science. 1984; 225: 1168.
10.1126/science.6474172
CAS PubMed Web of Science® Google Scholar

Citing Literature

Volume28, Issue3

March 2021

Pages 735-744

Associations between texture of T1-weighted magnetic resonance imaging and radiographic pathologies in Alzheimer’s disease

Abstract

Background and purpose

Methods

Results

Conclusions

CONFLICTS OF INTEREST

Open Research

DATA AVAILABILITY STATEMENT

Supporting Information

References

Citing Literature

References

Information

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Associations between texture of T1-weighted magnetic resonance imaging and radiographic pathologies in Alzheimer’s disease

Abstract

Background and purpose

Methods

Results

Conclusions

CONFLICTS OF INTEREST

Open Research

DATA AVAILABILITY STATEMENT

Supporting Information

References

Citing Literature

References

Related

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