Fig. 1: Schematic of using deep learning-based hierarchical clustering to define clusters of MPDs. | Molecular Psychiatry

Fig. 1: Schematic of using deep learning-based hierarchical clustering to define clusters of MPDs.

From: Identifying and validating subtypes within major psychiatric disorders based on frontal–posterior functional imbalance via deep learning

Fig. 1

Step one: identification of significant functional alterations in MPDs and using AutoEncoder to reduce the dimension of the identified alterations to d ∈ [2,10]. Step two: for each of the nine low-dimensional data from step one, we obtained nine different class labels based on clustering analyses, and five clusters (cluster A, B, C, D, and E) were identified. Step three: we performed the clusters merging process according to six runs of clustering and obtained two final subtypes. Furthermore, the subtypes varied in patterns of amplitude of low-frequency fluctuation alterations as compared to HC (voxel p < 0.001 with Gaussian random field correction for cluster p < 0.05). MPD major psychiatric disorder; HC healthy control; L left; R right; d dimension.

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