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

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TitleIdentifying sub-populations via unsupervised cluster analysis on multi-edge similarity graphs.
Publication TypeJournal Article
Year of Publication2012
AuthorsIngalhalikar, M, Smith, AR, Bloy, L, Gur, R, Roberts, TPL, Verma, R
JournalMed Image Comput Comput Assist Interv
Volume15
IssuePt 2
Pagination254-61
Date Published2012
KeywordsAdolescent, Algorithms, Artificial Intelligence, Brain, Child, Child Development Disorders, Pervasive, Child, Preschool, Connectome, Diffusion Magnetic Resonance Imaging, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Infant, Nerve Net, Pattern Recognition, Automated, Reproducibility of Results, Schizophrenia, Sensitivity and Specificity, Young Adult
Abstract

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.

Alternate JournalMed Image Comput Comput Assist Interv
PubMed ID23286056
PubMed Central IDPMC4023482
Grant ListR01 DC008871 / DC / NIDCD NIH HHS / United States
DC008871 / DC / NIDCD NIH HHS / United States
R01 MH092862 / MH / NIMH NIH HHS / United States
MH092862 / MH / NIMH NIH HHS / United States
R01 MH079938 / MH / NIMH NIH HHS / United States
MH079938 / MH / NIMH NIH HHS / United States
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