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Author Ealbert, Reka ♦ Rajtmajer, Sarah M. ♦ Molenaar, Peter C.M. ♦ Hillary, Frank Gerard ♦ Eroy, Arnab
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Frontiers Media S.A.
File Format HTM / HTML
Date Created 2015-09-11
Copyright Year ©2015
Language English
Subject Domain (in LCC) RC321-571 ♦ QM1-695
Subject Keyword Neuropsychiatry ♦ Biological psychiatry ♦ Neurosciences ♦ Brain Injury ♦ Human anatomy ♦ Modularity ♦ Graph theory ♦ Voxelwise region of interest selection ♦ Science ♦ Internal medicine ♦ Plasticity ♦ Medicine
Abstract Despite exciting advances in the functional imaging of the brain, it remains a challenge to define regions of interest (ROIs) that do not require investigator supervision and permit examination of change in networks over time (or plasticity). Plasticity is most readily examined by maintaining ROIs constant via seed-based and anatomical-atlas based techniques, but these approaches are not data-driven, requiring definition based on prior experience (e.g. choice of seed-region, anatomical landmarks). These approaches are limiting especially when functional connectivity may evolve over time in areas that are finer than known anatomical landmarks or in areas outside predetermined seeded regions. An ideal method would permit investigators to study network plasticity due to learning, maturation effects, or clinical recovery via multiple time point data that can be compared to one another in the same ROI while also preserving the voxel-level data in those ROIs at each time point. Data-driven approaches (e.g., whole-brain voxelwise approaches) ameliorate concerns regarding investigator bias, but the fundamental problem of comparing the results between distinct data sets remains. In this paper we propose an approach, aggregate-initialized label propagation (AILP), which allows for data at separate time points to be compared for examining developmental processes resulting in network change (plasticity). To do so, we use a whole-brain modularity approach to parcellate the brain into anatomically constrained functional modules at separate time points and then apply the AILP algorithm to form a consensus set of ROIs for examining change over time. To demonstrate its utility, we make use of a known dataset of individuals with traumatic brain injury sampled at two time points during the first year of recovery and show how the AILP procedure can be applied to select regions of interest to be used in a graph theoretical analysis of plasticity.
ISSN 16625129
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2015-07-01
e-ISSN 16625129
Journal Frontiers in Neuroanatomy
Volume Number 9


Source: Directory of Open Access Journals (DOAJ)