title: Visualization and unsupervised predictive clustering of high-dimensional multimodal neuroimaging data.
abstract: BACKGROUND
  Neuroimaging machine learning studies have largely utilized supervised algorithms - meaning they require both neuroimaging scan data and corresponding target variables (e.g. healthy vs. diseased) to be successfully 'trained' for a prediction task. Noticeably, this approach may not be optimal or possible when the global structure of the data is not well known and the researcher does not have an a priori model to fit the data.
  
  
  METHODS
  We set out to investigate the utility of an unsupervised machine learning technique; t-distributed stochastic neighbour embedding (t-SNE) in identifying 'unseen' sample population patterns that may exist in high-dimensional neuroimaging data. Multimodal neuroimaging scans from 92 healthy subjects were pre-processed using atlas-based methods, integrated and input into the t-SNE algorithm. Patterns and clusters discovered by the algorithm were visualized using a 2D scatter plot and further analyzed using the K-means clustering algorithm.
  
  
  METHODS
  t-SNE was evaluated against classical principal component analysis.
  
  
  CONCLUSIONS
  Remarkably, based on unlabelled multimodal scan data, t-SNE separated study subjects into two very distinct clusters which corresponded to subjects' gender labels (cluster silhouette index value=0.79). The resulting clusters were used to develop an unsupervised minimum distance clustering model which identified 93.5% of subjects' gender. Notably, from a neuropsychiatric perspective this method may allow discovery of data-driven disease phenotypes or sub-types of treatment responders.
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA. Electronic address: benson.irungu@uth.tmc.edu.
authors.forename: Benson
authors.initials: B
authors.lastname: Mwangi
authors.affiliation: UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
authors.forename: Jair C
authors.initials: JC
authors.lastname: Soares
authors.affiliation: The University of Texas Health Science Center at Houston, Department of Diagnostic & Interventional Imaging, Houston, TX, USA.
authors.forename: Khader M
authors.initials: KM
authors.lastname: Hasan
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D064847:Multimodal Imaging / Q000379:methods*; D059906:Neuroimaging / Q000379:methods*; D025341:Principal Component Analysis; D012727:Sex Characteristics; D013269:Stochastic Processes; D055815:Young Adult
keywords: Big data; Multimodal neuroimaging; Research domain criteria (RDoC); Unsupervised machine learning; t-Distributed stochastic neighbour embedding (t-SNE)