Identifying sub-populations via unsupervised cluster analysis on multi-edge similarity graphs

• Published in: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 15; no. Pt 2; p. 254
• Main Authors: Ingalhalikar, Madhura, Smith, Alex R, Bloy, Luke, Gur, Ruben, Roberts, Timothy P L, Verma, Ragini
• Format: Journal Article
• Language: English
• Published: Germany 2012
• Subjects: Adolescent; Algorithms; Artificial Intelligence; Brain - pathology; Child; Child Development Disorders, Pervasive - pathology; Child, Preschool; Connectome - methods; Diffusion Magnetic Resonance Imaging - methods; Female; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Infant; Nerve Net - pathology; Pattern Recognition, Automated - methods; Reproducibility of Results; Schizophrenia - pathology; Sensitivity and Specificity; Young Adult

Pathologies like autism and schizophrenia are a broad set of disorders with multiple etiologies in the same diagnostic category. This paper presents a method for unsupervised cluster analysis using multi-edge similarity graphs that combine information from different modalities. The method alleviates the issues with traditional supervised classification methods that use diagnostic labels and are therefore unable to exploit or elucidate the underlying heterogeneity of the dataset under analysis. The framework introduced in this paper has the ability to employ diverse features that define different aspects of pathology obtained from different modalities to create a multi-edged graph on which clustering is performed. The weights on the multiple edges are optimized using a novel concept of 'holding power' that describes the certainty with which a subject belongs to a cluster. We apply the technique to two separate clinical populations of autism spectrum disorder (ASD) and schizophrenia (SCZ), where the multi-edged graph for each population is created by combining information from structural networks and cognitive scores. For the ASD-control population the method clusters the data into two classes and the SCZ-control population is clustered into four. The two classes in ASD agree with underlying diagnostic labels with 92% accuracy and the SCZ clustering agrees with 78% accuracy, indicating a greater heterogeneity in the SCZ population.