Identifying Sub-Populations via Unsupervised Cluster Analysis on Multi-Edge Similarity Graphs

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.

Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

“…Autism is a heterogeneous developmental disorder with a range of symptom expression profiles, and rs-fMRI may have a role in explicating the root of this heterogeneity. Autism studies using other modalities have already had success using techniques that may accomplish this task ( Ingalhalikar et al 2012 ; Hu, and Lai, 2013 ). We anticipate that basic and clinical advances will result from studying functional brain networks with multivariate techniques.…”

“…Recent studies applied multivariate classification techniques to neuroimaging data to characterize ASD using features that are predictive of a diagnosis on the level of individuals. These classifier studies achieved relatively high classification accuracy (~60–85%) using multiple imaging modalities including structural MRI ( Sato et al, 2013 ; Ecker et al, 2010 ), diffusion tensor MRI (DTI) ( Ingalhalikar et al 2012 ; Lange et al, 2010 ), magnetoencephalography ( Roberts et al, 2011 ) and resting-state functional MRI (rs-fMRI; which measures “functional connectivity”, correlations between spontaneous BOLD signal fluctuations in different brain regions) ( Uddin et al, 2013 ; Nielsen and Zielinski, 2013 ; Anderson et al, 2011 ). Rs-fMRI is a particularly interesting technique as it can investigate, in a task-independent manner, the hypothesis that ASD involves the disruption of large-scale brain networks ( Castelli et al, 2002 ; Belmonte et al, 2004 ).…”

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

“…An automatic classification of schizophrenia subtypes has been rarely studied. Ingalhalikar et al proposed unsupervised spectral clustering of multi-edge graphs built from a structural connectivity network among 78 ROIs be usedto identify subtypes of autism and schizophrenia (Ingalhalikar et al, 2012). Gould et al proposed using whole brain, voxel-based morphometry to classify schizophrenia patients with cognitive deficit from those that are cognitively spared (Gould et al, 2014).…”

Topological network measures for drug repositioning

“…For example, for a PPI network, edges may denote physical interactions, but can also be used to denote co-expression networks, or protein complexes and their interactions [ 29 ]. Most methods that were used so far had been for graphs homogenous edge types, however, recent efforts have been focused on analyzing multi-edged graphs, or ‘multi-graphs.’ For example, a multi-edged graph arose from imaging and cognitive data in a study of autism and schizophrenia, on which unsupervised clustering was performed to obtain subpopulations [ 30 ]. Extraction of data from biomedical literature yields multi-edged graphs or ‘Knowledge graphs’ [ 31 , 32 ].…”