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Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach

Background: Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons o...

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Hlavní autoři: Pfarr, Julia-Katharina, Meller, Tina, Brosch, Katharina, Stein, Frederike, Thomas-Odenthal, Florian, Evermann, Ulrika, Wroblewski, Adrian, Ringwald, Kai G., Hahn, Tim, Meinert, Susanne, Winter, Alexandra, Thiel, Katharina, Flinkenflügel, Kira, Jansen, Andreas, Krug, Axel, Dannlowski, Udo, Kircher, Tilo, Gaser, Christian, Nenadi´c, Igor
Médium: Článek
Jazyk:angličtina
Vydáno: Philipps-Universität Marburg 2023
Témata:
Cluster analysis, Gyrification, Data-driven approach, Multivariate statistics, Transdiagnostic
Medizin
Medical sciences Medicine
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Popis
Shrnutí:Background: Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking. Methods: In this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories. Results: Cluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors. Conclusions: Results indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.
Popis jednotky:Gefördert durch den Open-Access-Publikationsfonds der UB Marburg.
Fyzický popis:11 Seiten
DOI:10.1016/j.neuroimage.2023.120349

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