Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia.
Hum Brain Mapp
; 44(17): 5828-5845, 2023 12 01.
Article
en En
| MEDLINE
| ID: mdl-37753705
ABSTRACT
This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. We apply our proposed framework, which disentangles multimodal data into private and shared sets of features from pairs of structural (sMRI), functional (sFNC and ICA), and diffusion MRI data (FA maps). With our approach, we find that heterogeneity in schizophrenia is potentially a function of modality pairs. Results show (1) schizophrenia is highly multimodal and includes changes in specific networks, (2) non-linear relationships with schizophrenia are observed when interpolating among shared latent dimensions, and (3) we observe a decrease in the modularity of functional connectivity and decreased visual-sensorimotor connectivity for schizophrenia patients for the FA-sFNC and sMRI-sFNC modality pairs, respectively. Additionally, our results generally indicate decreased fractional corpus callosum anisotropy, and decreased spatial ICA map and voxel-based morphometry strength in the superior frontal lobe as found in the FA-sFNC, sMRI-FA, and sMRI-ICA modality pair clusters. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data which we hope challenges the reader to think differently about how modalities interact.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Esquizofrenia
Límite:
Humans
Idioma:
En
Año:
2023
Tipo del documento:
Article