Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease

Schmidt-Richberg, Alexander; Ledig, Christian; Molina‐Abril, Helena; Frangi, Alejandro F.; Rueckert, Daniel

doi:10.1007/978-3-319-19992-4_30

Cited by 18 publications

(24 citation statements)

References 11 publications

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“…Furthermore, a modified version of the LMS method, that uses the Yeo-Johnson [52] power transform as in [51] to allow the handling of non-positive marker values, can be used to induce Gaussianity in the generally non-Gaussian marker distribution data and obtain the conditional crosssectional distributions. In the same way as in [41], the conditional crosssectional distributions, obtained from the modified LMS method [51], for each of the groups (CN-MCI and MCI-AD) can be used for the temporal alignment of markers. The area under the curve of the conditional distributions of the markers at each time (after within-group temporal alignment) are used as goodness of fit, and its maximization determines the temporal shift between the CN-MCI and MCI-AD subject groups.…”

Section: Temporal Alignment Of Markersmentioning

confidence: 99%

“…Some evidence also suggests a sigmoidal pattern, with an acceleration phase during the early stages of the disease and deceleration at later stages, for the dynamic behavior of cortical thinning and hippocampal volume loss [39,7]. However, in [30,12,41] little evidence of acceleration, that would suggest a non-linear effect, in structural brain atrophy rates was observed. In this work we use a linear function to model the dynamic changes of structural MR volumetric markers.…”

Section: Mixed Effects Modelingmentioning

confidence: 99%

“…Detailed studies into early state longitudinal Alzheimer's disease marker trajectory dynamics, using data-driven methods, have the potential to aid the effort in the development of measures that can accurately and robustly quantify indications of the disease, even before its presymtomatic and preclinal stages. Previously, hypothetical [23,24] and experimental models [13,14,50,10,20,7,25,41,21,4,5,1,12,15,53] of disease progression based on Alzheimer's disease markers, such as cerebrospinal fluid (CSF), imaging and cognitive markers have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several approaches that regard the disease progression trajectory as a continuous process have been developed [13,14,50,10,20,7,25,41,21,4,5,1,12]. Many of the proposed frameworks rely on modeling cognitive scores such as the mini mental state examination (MMSE), Alzheimer's disease assessment scale (ADAS), clinical dementia rating sum of boxes (CDRSB), or Rey's audio visual learning test (RAVLT) among others, as surrogate measures of disease progression.…”

Section: Introductionmentioning

confidence: 99%

“…Jedynak et al [25] proposed a statistical methodology using multiple Alzheimer's disease markers, which produces an Alzheimer's disease progression score for each patient and each time point in the database. Schmidt-Richberg et al [41] proposed an approach to estimate typical trajectories of several markers over the full course of the disease to estimate current disease progress and disease progression rate.…”

Section: Introductionmentioning

confidence: 99%

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Instantiated mixed effects modeling of Alzheimer's disease markers

et al. 2016

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The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease is of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive a global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified "marker signature" that defines a subpopulation of similar * Corresponding author: Ricardo Guerrero Email address: reg09@imperial.ac.uk (R. Guerrero) 1 This project was partially funded by the Innovate UK (formerly Technology Strategy Board -TSB).2 Data used in the preparation of this article was obtained from the ADNI database (http://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 Preprint submitted to NeuroImage June 27, 2016 cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models.

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Section: Temporal Alignment Of Markersmentioning

confidence: 99%

Section: Mixed Effects Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Instantiated mixed effects modeling of Alzheimer's disease markers

et al. 2016

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7 Years of Developing Seed Techniques for Alzheimer’s Disease Diagnosis Using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis

Soussia

Rekik

2019

Predictive Intelligence in Medicine

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A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images

Marinescu

Eshaghi

Lorenzi

et al. 2017

Lecture Notes in Computer Science

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Abstract. We present a disease progression model with single vertex resolution that we apply to cortical thickness data. Our model works by clustering together vertices on the cortex that have similar temporal dynamics and building a common trajectory for vertices in the same cluster. The model estimates optimal stages and progression speeds for every subject. Simulated data show that it is able to accurately recover the vertex clusters and the underlying parameters. Moreover, our clustering model finds similar patterns of atrophy for typical Alzheimer's disease (tAD) subjects on two independent datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and a cohort from the Dementia Research Centre (DRC), UK. Using a separate set of subjects with Posterior Cortical Atrophy (PCA) from the DRC dataset, we also show that the model finds different patterns of atrophy in PCA compared to tAD. Finally, our model also provides a novel way to parcellate the brain based on disease dynamics.

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Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease

Cited by 18 publications

References 11 publications

Instantiated mixed effects modeling of Alzheimer's disease markers

Instantiated mixed effects modeling of Alzheimer's disease markers

7 Years of Developing Seed Techniques for Alzheimer’s Disease Diagnosis Using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis

A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images

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