scholarly journals Surface-based analysis of cortical thickness and volume loss in Alzheimer’s disease

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Emily Iannopollo ◽  
Ryan Plunkett ◽  
Kara Garcia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Cortical Thickness ◽  
Surface Matching ◽  
Cortical Surface ◽  
Volume Loss ◽  
Cortical Volume ◽  
Human Connectome Project ◽  
And Control

Background and Hypothesis: Magnetic resonance imaging (MRI) has become a useful tool in monitoring the progression of Alzheimer's disease. Previous surface-based analysis has focused on changes in cortical thickness associated with the disease1. The objective of this study is to analyze MRI-derived cortical reconstructions for patterns of atrophy in terms of both cortical thickness and cortical volume. We hypothesize that Alzheimer’s Disease progression will be associated with a more significant change in volume than thickness. Experimental Design or Project Methods: MRI data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). All subjects with baseline and two-year 3T MRI scans were included. Segmentation of MRIs into gray and white matter was performed with FreeSurfer2,3,4,5. Subjects whose scans did not segment accurately were excluded. Surfaces were then registered to a common atlas with Ciftify6, and anatomically-constrained Multimodal Surface Matching (aMSM) was used to analyze longitudinal changes in each subject7. This produced continuous surface maps showing changes in cortical surface area and thickness. These maps were multiplied to create cortical volume maps8. Permutation Analysis of Linear Models (PALM) was used to perform two-sample t-tests comparing the maps of the Alzheimer’s and control groups9. Results: Preliminary analysis of nine Alzheimer’s subjects and nine control subjects produced surface maps displaying patterns that were expected given previous research findings10,11. There was increased volume and thickness loss in Alzheimer’s subjects relative to controls, with relatively high loss in structures of the medial temporal lobe. Future analysis of a larger sample will determine whether statistically significant differences exist between the Alzheimer’s and control groups in terms of thickness loss and volume loss. Conclusion and Potential Impact: If significant results are found, surface-based analysis of cortical volume may allow for detection of atrophy at an earlier stage in disease progression than would be possible based on cortical thickness.   References 1. Clarkson MJ, Cardoso MJ, Ridgway GR, Modat M, Leung KK, Rohrer JD, Fox NC, Ourselin S. A comparison of voxel and surface based cortical thickness estimation methods. NeuroImage. 2011 Aug 1; 57(3):856-65. 2. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179194. 3. Fischl B, Sereno M, Dale A. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207.  4. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341-355. 5. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM. Sequence-independent segmentation of magnetic resonance images. Neuroimage 2004;23 Suppl 1:S69-84. 6. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M, Polimeni JR, Van Essen DC, Jenkinson M, WU-Minn HCP Consortium. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013 Oct 15;80:105-24. 7. Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L, Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, Rueckert D. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453-65. 8. Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC. Informatics and data mining tools and strategies for the human connectome project. Front Neuroinform 2011;5:4. 9. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397 10. Matsuda, H. MRI morphometry in Alzheimer’s disease. Ageing Research Reviews. 2016 Sep;30:17-24. 11. Risacher SL, Shen L, West JD, Kim S, McDonald BC, Beckett LA, Harvey DJ, Jack CR Jr, Weiner MW, Saykin AJ. Alzheimer's Disease Neuroimaging Initiative (ADNI). Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging. 2010 Aug;31(8):1401-18. 

IC-P-049: Exploring the biomarker cascade model: Relating cortical volume loss to [11C]PiB, [18F]FDG and MMSE over time in Alzheimer's disease patients and normal controls

2012 ◽  
Vol 8 (4S_Part_1) ◽  
pp. P32-P32
Author(s):  
Sofie Adriaanse ◽  
Koene van Dijk ◽  
Rik Ossenkoppele ◽  
Martin Reuter ◽  
Nelleke Tolboom ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cascade Model ◽  
Volume Loss ◽  
Cortical Volume ◽  
18F Fdg ◽  
Normal Controls ◽  
Over Time

scholarly journals Effects of Moderate Intensity Exercise on the Cortical Thickness and Subcortical Volumes of Preclinical Alzheimer’s Disease Patients: A Pilot Study

2020 ◽  
Vol 17 (6) ◽  
pp. 613-619
Author(s):  
Yoo Hyun Um ◽  
Sheng-Min Wang ◽  
Nak-Young Kim ◽  
Dong Woo Kang ◽  
Hae-Ran Na ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Cortical Thickness ◽  
Exercise Group ◽  
Moderate Intensity ◽  
Preclinical Alzheimer’S Disease ◽  
Moderate Intensity Exercise ◽  
Preclinical Ad ◽  
Subcortical Volumes

Objective We aimed to explore the impact of moderate intensity exercise on the cortical thickness and subcortical volumes of preclinical Alzheimer’s disease (AD) patients.Methods Sixty-three preclinical AD patients with magnetic resonance imaging (MRI) and 18-florbetaben positron emission tomography (PET) data were enrolled in the study. Information on demographic characteristics, cognitive battery scores, self-reported exercise habits were attained. Structural magnetic resonance images were analyzed and processed using Freesurfer v6.0.Results Compared to Exercise group, Non-Exercise group demonstrated reduced cortical thickness in left parstriangularis, rostral middle frontal, entorhinal, superior frontal, lingual, superior parietal, lateral occipital, inferior parietal gyrus, temporal pole, precuneus, insula, fusiform gyrus, right precuneus, superiorparietal, lateral orbitofrontal, rostral middle frontal, medial orbitofrontal, superior frontal, lingual, middle temporal gyrus, insula, supramarginal, parahippocampal, paracentral gyrus. Volumes of right thalamus, caudate, putamen, pallidum, hippocampus, amygdala were also reduced in Non-Exercise group.Conclusion Moderate intensity exercise affects cortical and subcortical structures in preclinical AD patients. Thus, physical exercise has a potential to be an effective intervention to prevent future cognitive decline in those at high risk of AD.

P2-247: Exploring the biomarker cascade model: Relating cortical volume loss to [11C]PIB, [18F]FDG and MMSE over time in Alzheimer's disease patients and normal controls

2012 ◽  
Vol 8 (4S_Part_9) ◽  
pp. P348-P348
Author(s):  
Sofie Adriaanse ◽  
Koene van Dijk ◽  
Rik Ossenkoppele ◽  
Martin Reuter ◽  
Nelleke Tolboom ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cascade Model ◽  
Volume Loss ◽  
Cortical Volume ◽  
18F Fdg ◽  
Normal Controls ◽  
Over Time

SPECIFIC PROPERTIES OF TUBULIN IN THE PREFRONTAL CORTEX IN CONTROL, SCHIZOPHRENIA AND VASCULAR DEMENTIA

2020 ◽  
pp. 65-71
Author(s):  
Burbaeva G.Sh. ◽  
Androsova L.V. ◽  
Vorobyeva E.A. ◽  
Savushkina O.K.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prefrontal Cortex ◽  
Vascular Dementia ◽  
Binding Activity ◽  
Nephelometric Method ◽  
Rate Of Polymerization ◽  
Mental Diseases ◽  
And Control ◽  
The Brain

The aim of the study was to evaluate the rate of polymerization of tubulin into microtubules and determine the level of colchicine binding (colchicine-binding activity of tubulin) in the prefrontal cortex in schizophrenia, vascular dementia (VD) and control. Colchicine-binding activity of tubulin was determined by Sherlinе in tubulin-enriched extracts of proteins from the samples. Measurement of light scattering during the polymerization of the tubulin was carried out using the nephelometric method at a wavelength of 450-550 nm. There was a significant decrease in colchicine-binding activity and the rate of tubulin polymerization in the prefrontal cortex in both diseases, and in VD to a greater extent than in schizophrenia. The obtained results suggest that not only in Alzheimer's disease, but also in other mental diseases such as schizophrenia and VD, there is a decrease in the level of tubulin in the prefrontal cortex of the brain, although to a lesser extent than in Alzheimer's disease, and consequently the amount of microtubules.

Prevalence of Sinusitis Detected by Magnetic Resonance Imaging in Subjects with Dementia or Alzheimer’s Disease

2015 ◽  
Vol 12 (10) ◽  
pp. 1006-1011 ◽  
Author(s):  
Minori Yasue ◽  
Saiko Sugiura ◽  
Yasue Uchida ◽  
Hironao Otake ◽  
Masaaki Teranishi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Resonance Imaging

Amyloid Beta-Weighted Cortical Thickness: A New Imaging Biomarker in Alzheimer's Disease

2015 ◽  
Vol 12 (6) ◽  
pp. 563-571 ◽  
Author(s):  
Chan Kim ◽  
Jihye Hwang ◽  
Jong-Min Lee ◽  
Jee Hoon Roh ◽  
Jae-Hong Lee ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cortical Thickness ◽  
Amyloid Beta ◽  
Imaging Biomarker

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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