Lea Baecker
King's College London
7 Papers
Lea Baecker is an academic researcher from King's College London. The author has contributed to research in topics: Normative & Neuroimaging. The author has an hindex of 5, co-authored 7 publications.
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Papers
Machine learning for brain age prediction: Introduction to methods and clinical applications
Lea Baecker,Rafael Garcia-Dias,Sandra Vieira,Cristina Scarpazza,Cristina Scarpazza,Andrea Mechelli +5 more
TL;DR: A review of the state-of-the-art methods and potential clinical applications of brain age prediction can be found in this paper, where a regression machine learning model of age-related neuroanatomical changes in healthy people is used to predict brain age.
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Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.
Lea Baecker,Jessica Dafflon,Pedro F. da Costa,Rafael Garcia-Dias,Sandra Vieira,Cristina Scarpazza,Cristina Scarpazza,Vince D. Calhoun,Vince D. Calhoun,João Ricardo Sato,Andrea Mechelli,Walter H. L. Pinaya,Walter H. L. Pinaya +12 more
TL;DR: In this paper, the authors compared the performance of support vector regression, relevance vector regression and Gaussian process regression on whole-brain region-based or voxel-based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis.
Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.
Walter H. L. Pinaya,Walter H. L. Pinaya,Cristina Scarpazza,Cristina Scarpazza,Rafael Garcia-Dias,Sandra Vieira,Lea Baecker,Pedro F. da Costa,Pedro F. da Costa,Alberto Redolfi,Giovanni B. Frisoni,Michela Pievani,Vince D. Calhoun,João Ricardo Sato,Andrea Mechelli +14 more
TL;DR: In this article, the authors used deep autoencoders to assess how individuals deviated from the healthy norm and established which brain regions were associated with this deviation. And they found that patients exhibited deviations according to the severity of their clinical condition.
Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders.
Cristina Scarpazza,Cristina Scarpazza,M. Ha,Lea Baecker,Rafael Garcia-Dias,Walter H. L. Pinaya,Walter H. L. Pinaya,Sandra Vieira,Andrea Mechelli +8 more
TL;DR: This systematic review describes and compares the technical characteristics of the available tools, and proposes a checklist of pivotal characteristics that should be included in an “ideal” neuroimaging-based clinical tool for brain disorders.
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Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer’s disease
Walter H. L. Pinaya,Cristina Scarpazza,Rafael Garcia-Dias,Sandra Vieira,Lea Baecker,Pedro F. da Costa,Alberto Redolfi,Giovanni B. Frisoni,Michela Pievani,Vince D. Calhoun,João Ricardo Sato,Andrea Mechelli,Alzheimer’s Disease Neuroimaging Initiative,Australian Imaging Biomarkers +13 more
TL;DR: This study assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer’s disease and mild cognitive impairment to find that patients exhibited deviations according to the severity of their clinical condition.