Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Search: onr:"swepub:oai:DiVA.org:umu-203621" > Interpretable and s...

1 of 1
Previous record
Next record
To hitlist

Details
MARC

De Pierrefeu, AmicieNeuroSpin, CEA, Paris-Saclay, France (author)

Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity

Article/chapterEnglish2018

Publisher, publication year, extent ...

IEEE,2018
printrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:umu-203621
https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-203621URI
https://doi.org/10.1109/PRNI.2018.8423946DOI

Supplementary language notes

Language:English
Summary in:English

Part of subdatabase

SwePubSwePub

Classification

Subject category:ref swepub-contenttype
Subject category:kon swepub-publicationtype

Notes

The use of machine-learning (ML) in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Indeed, ML algorithms can jointly examine all brain features to capture complex relationships in the data in order to make inferences at a single-subject level. To deal with such high dimensional input and the associated risk of overfitting on the training data, a proper regularization (or feature selection) is required. Standard ℓ2-regularized predictors, such as Support Vector Machine, provide dense patterns of predictors. However, in the context of predictive disease signature discovery, it is now essential to understand the brain pattern that underpins the prediction. Despite ℓ1-regularized (sparse) has often been advocated as leading to more interpretable models, they generally lead to scattered and unstable patterns. We hypothesize that the integration of prior knowledge regarding the structure of the input images should improve the relevance and the stability of the predictive signature. Such structured sparsity can be obtained by combining together ℓ1 (possibly ℓ2) and Total variation (TV) penalties. We demonstrated the relevance of using ML with structured sparsity on a large multisite dataset of schizophrenia patients and controls. Using 3D maps of grey matter density, we obtained promising inter-site prediction performances. More importantly, we have uncovered a predictive signature of schizophrenia that is clinically interpretable and stable across resampling. This suggests that structured sparsity provides a major breakthrough over 'off-The-shelf' algorithms to perform a robust selection of important brain regions in the context of biomarkers discovery.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

Löfstedt, TommyUmeå universitet,Institutionen för strålningsvetenskaper(Swepub:umu)toklot02 (author)
Laidi, CharlesNeuroSpin, CEA, Paris-Saclay, France; Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Cŕeteil, France; Pole de Psychiatrie, Assistance Publique Hopitaux de Paris (AP-HP), Faculté de Médecine de Cŕeteil, Cŕeteil, France (author)
Hadj-Selem, FouadEnergy Transition Institute: VeDeCoM, France (author)
Leboyer, MarionInstitut National de la Santé et de la Recherche Médicale (INSERM), Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Cŕeteil, France; Pole de Psychiatrie, Assistance Publique Hopitaux de Paris (AP-HP), Faculté de Médecine de Cŕeteil, Cŕeteil, France (author)
Ciuciu, PhilippeNeuroSpin, CEA, Paris-Saclay, France (author)
Houenou, JosselinNeuroSpin, CEA, Paris-Saclay, France; Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Cŕeteil, France; Pole de Psychiatrie, Assistance Publique Hopitaux de Paris (AP-HP), Faculté de Médecine de Cŕeteil, Cŕeteil, France (author)
Duchesnay, EdouardNeuroSpin, CEA, Paris-Saclay, France (author)
NeuroSpin, CEA, Paris-Saclay, FranceInstitutionen för strålningsvetenskaper (creator_code:org_t)

Related titles

In:2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI): IEEE9781538668597

Internet link

Find in a library

2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI) (Search for host publication in LIBRIS)

To the university's database

1 of 1
Previous record
Next record
To hitlist

Find more in SwePub

By the author/editor: De Pierrefeu, Am ...; Löfstedt, Tommy; Laidi, Charles; Hadj-Selem, Foua ...; Leboyer, Marion; Ciuciu, Philippe; show more...; Houenou, Josseli ...; Duchesnay, Edoua ...; show less...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Bioinformatics

Articles in the publication: 2018 Internation ...

By the university: Umeå University

Search outside SwePub

Extend your search to:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se