Evidence map›Paper›PMID 35546881›Full record

ArticleFrontiers in neuroscience2022

Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification.

Wouter van Loon, Frank de Vos, Marjolein Fokkema, Botond Szabo, Marisa Koini, Reinhold Schmidt, Mark de Rooij

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Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Wouter van LoonDepartment of Methodology and Statistics, Leiden University, Leiden, Netherlands.
Frank de VosDepartment of Methodology and Statistics, Leiden University, Leiden, Netherlands.
Marjolein FokkemaDepartment of Methodology and Statistics, Leiden University, Leiden, Netherlands.
Botond SzaboDepartment of Decision Sciences, Bocconi University, Milan, Italy.
Marisa KoiniDivision of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria.
Reinhold SchmidtDivision of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria.
Mark de RooijDepartment of Methodology and Statistics, Leiden University, Leiden, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

Indexed as

feature selectionmachine learningmultimodal MRIpenalized regressionstacked generalization

Identifiers

PMID35546881
PMCPMC9082949

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.