Titre
Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Paunova, R.
Auteure/Auteur
Ramponi, C.
Auteure/Auteur
Kandilarova, S.
Auteure/Auteur
Todeva-Radneva, A.
Auteure/Auteur
Latypova, A.
Auteure/Auteur
Stoyanov, D.
Auteure/Auteur
Kherif, F.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1664-0640
Statut éditorial
Publié
Date de publication
2023-10
Volume
14
Première page
1272933
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Résumé
In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54).
We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.
As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.
Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.
We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.
As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.
Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.
PID Serval
serval:BIB_2E68B63EA815
PMID
Open Access
Oui
Date de création
2023-11-06T12:17:45.339Z
Date de création dans IRIS
2025-05-20T17:49:18Z
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Nom
37908595_BIB_2E68B63EA815.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
1.06 MB
Format
Adobe PDF
PID Serval
serval:BIB_2E68B63EA815.P001
URN
urn:nbn:ch:serval-BIB_2E68B63EA8158
Somme de contrôle
(MD5):9b2bbfa653f38a92a88ad31ce9911cb3