title: Constructing the resting state structural connectome.
abstract: BACKGROUND
  Many recent studies have separately investigated functional and white matter (WM) based structural connectivity, yet their relationship remains less understood. In this paper, we proposed the functional-by-structural hierarchical (FSH) mapping to integrate multimodal connectome data from resting state fMRI (rsfMRI) and the whole brain tractography-derived connectome.
  
  
  METHODS
  FSH first observes that the level of resting-state functional correlation between any two regions in general decreases as the graph distance of the corresponding structural connectivity matrix between them increases. As not all white matter tracts are actively in use (i.e., "utilized") during resting state, FSH thus models the rsfMRI correlation as an exponential decay function of the graph distance of the rsfMRI-informed structural connectivity or rsSC. rsSC is mathematically computed by multiplying entry-by-entry the tractography-derived structural connectivity matrix with a binary white matter "utilization matrix" U. U thus encodes whether any specific WM tract is being utilized during rsFMRI, and is estimated using simulated annealing. We applied this technique and investigated the hierarchical modular structure of rsSC from 7 depressed subjects and 7 age/gender matched controls.
  
  
  RESULTS
  No significant group differences were detected in the modular structures of either the resting state functional connectome or the whole brain tractography-derived connectome. By contrast, FSH results revealed significantly different patterns of association in the bilateral posterior cingulate cortex and right precuneus, with the depressed group exhibiting stronger associations among regions instrumental in self-referential operations.
  
  
  CONCLUSIONS
  The results of this study support that enhanced sensitivity can be obtained by integrating multimodal imaging data using FSH, a novel computational technique that may increase power to detect group differences in brain connectomes.
authors.affiliation: Department of Psychiatry, University of Illinois Chicago, IL, USA.
authors.forename: Olusola
authors.initials: O
authors.lastname: Ajilore
authors.affiliation: Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, CA, USA.
authors.forename: Liang
authors.initials: L
authors.lastname: Zhan
authors.affiliation: Department of Psychiatry, University of Illinois Chicago, IL, USA ; Department of Electrical and Computer Engineering, University of Illinois Chicago, IL, USA.
authors.forename: Johnson
authors.initials: J
authors.lastname: Gadelkarim
authors.affiliation: Department of Psychiatry, University of Illinois Chicago, IL, USA.
authors.forename: Aifeng
authors.initials: A
authors.lastname: Zhang
authors.affiliation: UCLA Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, CA, USA.
authors.forename: Jamie D
authors.initials: JD
authors.lastname: Feusner
authors.affiliation: Department of Psychiatry, University of Illinois Chicago, IL, USA.
authors.forename: Shaolin
authors.initials: S
authors.lastname: Yang
authors.affiliation: Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, CA, USA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
authors.affiliation: Department of Psychiatry, University of Illinois Chicago, IL, USA.
authors.forename: Anand
authors.initials: A
authors.lastname: Kumar
authors.affiliation: Department of Psychiatry, University of Illinois Chicago, IL, USA ; Department of Bioengineering, University of Illinois Chicago, IL, USA ; Community Psychiatry Associates Sacramento, CA, USA.
authors.forename: Alex
authors.initials: A
authors.lastname: Leow
keywords: connectivity; fMRI; major depression; multimodal; neuroimaging