Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer’s disease

Holbrook, Andrew J.; Tustison, Nicholas J.; Márquez, Freddie; Roberts, Jared M.; Yassa, Michael A.; Gillen, Daniel L.

doi:10.1101/19011825

Cited by 9 publications

(12 citation statements)

References 52 publications

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“…Root mean square error (RMSE) between the actual and predicted ages are the quantity used for comparative evaluation. As we have explained previously 19 , we find these evaluation measures to be much more useful than other commonly applied criteria as they are closer to assessing the actual utility of these thickness measurements as biomarkers for disease 47 or growth. In recent work 44 the authors employ correlation with FreeSurfer thickness values as the primary evaluation for assessing relative performance with ANTs cortical thickness 19 .…”

Section: Resultsmentioning

confidence: 73%

The ANTsX ecosystem for quantitative biological and medical imaging

Tustison

Cook

Holbrook

et al. 2021

Sci Rep

Self Cite

120

125

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The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.

show abstract

Section: Resultsmentioning

confidence: 73%

The ANTsX ecosystem for quantitative biological and medical imaging

Tustison

Cook

Holbrook

et al. 2021

Sci Rep

Self Cite

120

125

View full text Add to dashboard Cite

show abstract

“…Root mean square error (RMSE) between the actual and predicted ages are the quantity used for comparative evaluation. As we have explained previously 20 , we find these evaluation measures to be much more useful than some other commonly applied criteria as they are closer to assessing the actual utility of these thickness measurements as actual biomarkers for disease 46 or growth. For example, in recent work 34 the authors employ correlation with FreeSurfer thickness values as the primary evaluation for assessing relative performance with ANTs cortical thickness 20 .…”

Section: Resultsmentioning

confidence: 74%

The ANTsX ecosystem for quantitative biological and medical imaging

Tustison

Holbrook³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open- source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI. Not only does this significantly improve computational efficiency and provide comparable-to-superior accuracy over the existing ANTs pipeline but it also illustrates the importance of the comprehensive ANTsX approach as a framework for medical image analysis.

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“…Another limitation is the absence of biomarker data concerning potential AD pathology. The grey matter differences that were observed in the ERC in our at-risk group may provide an indirect marker of potential AD pathology given prior work showing associations between ERC thickness and tau pathology (Holbrook et al, 2019;Velayudhan et al, 2013), but future work employing in vivo biomarkers is needed to address this question. Moreover, it is important to note that not every adult identified as at-risk in our study will move on to develop clinical memory impairment or an AD diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Medial temporal lobe structure, mnemonic and perceptual discrimination in healthy older adults and those at risk for mild cognitive impairment

Gellersen

Trelle

Farrar

et al. 2022

Preprint

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Cognitive tests sensitive to the integrity of the medial temporal lobe (MTL), such as mnemonic discrimination of perceptually similar stimuli, may be useful early markers of risk for cognitive decline in older populations. Perceptual discrimination of stimuli with overlapping features also relies on MTL, but remains relatively unexplored in this context. We assessed mnemonic discrimination in two test formats (Forced Choice, Yes/No) and perceptual discrimination of objects and scenes in 111 community-dwelling older adults at different risk status for cognitive impairment based on neuropsychological screening. We also investigated associations between performance and MTL subregion volume and thickness. The at-risk group exhibited reduced entorhinal thickness and impaired perceptual and mnemonic discrimination. Perceptual discrimination impairment partially explained group differences in mnemonic discrimination and correlated with entorhinal thickness. Executive dysfunction accounted for Yes/No deficits in at-risk adults, demonstrating the importance of test format for the interpretation of memory decline. These results suggest that perceptual discrimination tasks may be useful tools for detecting incipient cognitive impairment related to reduced MTL integrity in non-clinical populations.

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Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer’s disease

Cited by 9 publications

References 52 publications

The ANTsX ecosystem for quantitative biological and medical imaging

The ANTsX ecosystem for quantitative biological and medical imaging

The ANTsX ecosystem for quantitative biological and medical imaging

Medial temporal lobe structure, mnemonic and perceptual discrimination in healthy older adults and those at risk for mild cognitive impairment

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