Aperçu: G.M.
L'autisme
est un trouble du développement qui est actuellement diagnostiqué à
l'aide de tests comportementaux qui peuvent être subjectifs. Par conséquent, les biomarqueurs objectifs d'imagerie non invasive de l'autisme sont activement recherchés. Le
thème commun émergeant des études antérieures d'imagerie par résonance
magnétique fonctionnelle (IRMf) est que l'autisme est caractérisé par
des altérations des connexions fonctionnelles dérivées de l'IRMf dans
certains réseaux cérébraux qui peuvent constituer un biomarqueur pour le
diagnostic objectif. Cependant,
l'identification des personnes "avec autisme" uniquement en
fonction de ces mesures n'a pas été fiable, surtout si l'on tient compte
de la taille des échantillons.
Les chercheurs émettent l'hypothèse que les réseaux fonctionnels du cerveau qui sont les plus
reproductibles au sein des groupes avec autisme et des groupes témoins sans autisme pris séparément, mais pas lorsque les deux groupes sont fusionnés, peuvent
posséder la possibilité de distinguer efficacement les groupes.
Dans cette étude, les chercheurs utilisent un
schéma de "découverte-confirmation" basé sur l'évaluation de la
reproductibilité des composants indépendants obtenus à partir d'IRMf à l'état
de repos (découverte) suivi d'une analyse de clustering de ces
composants pour évaluer leur capacité à discriminer entre les groupes
dans un manière non supervisée (confirmation).
La méthode proposée a permis de caractériser la reproductibilité des
réseaux cérébrales dans l'autisme et pourrait éventuellement être
déployée dans d'autres troubles mentaux.
Front Neurosci. 2017 Sep 8;11:459. doi: 10.3389/fnins.2017.00459. eCollection 2017.
Investigating Brain Connectomic Alterations in Autism Using the Reproducibility of Independent Components Derived from Resting State Functional MRI Data
Syed MA1, Yang Z2,3, Hu XP4, Deshpande G5,6,7.
Abstract
Significance:
Autism is a developmental disorder that is currently diagnosed using
behavioral tests which can be subjective. Consequently, objective
non-invasive imaging biomarkers of Autism are being actively researched.
The common theme emerging from previous functional magnetic resonance
imaging (fMRI) studies is that Autism is characterized by alterations of
fMRI-derived functional connections in certain brain networks which may
provide a biomarker for objective diagnosis. However, identification of
individuals with Autism solely based on these measures has not been
reliable, especially when larger sample sizes are taken into
consideration.
Objective: We surmise that metrics derived from Autism subjects may not be highly reproducible within this group leading to poor generalizability. We hypothesize that functional brain networks that are most reproducible within Autism and healthy Control groups separately, but not when the two groups are merged, may possess the ability to distinguish effectively between the groups.
Methods: In this study, we propose a "discover-confirm" scheme based upon the assessment of reproducibility of independent components obtained from resting state fMRI (discover) followed by a clustering analysis of these components to evaluate their ability to discriminate between groups in an unsupervised way (confirm).
Results: We obtained cluster purity ranging from 0.695 to 0.971 in a data set of 799 subjects acquired from multiple sites, depending on how reproducible the corresponding components were in each group.
Conclusion: The proposed method was able to characterize reproducibility of brain networks in Autism and could potentially be deployed in other mental disorders as well.
Objective: We surmise that metrics derived from Autism subjects may not be highly reproducible within this group leading to poor generalizability. We hypothesize that functional brain networks that are most reproducible within Autism and healthy Control groups separately, but not when the two groups are merged, may possess the ability to distinguish effectively between the groups.
Methods: In this study, we propose a "discover-confirm" scheme based upon the assessment of reproducibility of independent components obtained from resting state fMRI (discover) followed by a clustering analysis of these components to evaluate their ability to discriminate between groups in an unsupervised way (confirm).
Results: We obtained cluster purity ranging from 0.695 to 0.971 in a data set of 799 subjects acquired from multiple sites, depending on how reproducible the corresponding components were in each group.
Conclusion: The proposed method was able to characterize reproducibility of brain networks in Autism and could potentially be deployed in other mental disorders as well.
KEYWORDS:
autism; clustering; fMRI; independent components; reproducibility- PMID:28943835
- PMCID:PMC5596295
- DOI:10.3389/fnins.2017.00459
