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.
Highlights
The human brain has ∼80 billion neurons each with between 100 and 1000 synaptic connections, making the task of modeling brain functioning the ultimate big data problem (HerculanoHouzel, 2009; Pakkenberg et al, 2003)
Label Propagation of Aggregated Data Label propagation was performed in order to reconstitute aROIs into functionally homogeneous lpROIs while preserving the original data at the voxel level for each time point
The current study presents a reliable approach to determine meaningful brain regions for the study of network plasticity
Summary
The human brain has ∼80 billion neurons each with between 100 and 1000 synaptic connections, making the task of modeling brain functioning the ultimate big data problem (HerculanoHouzel, 2009; Pakkenberg et al, 2003). While current functional brain imaging methods in humans (such as functional MRI) sample only a portion of this enormous network, including ∼20–40 thousand voxels with time varying signals, data analyses remain computationally challenging. Most approaches under-represent the richness of the data available. We focus our attention on bold oxygen level dependent functional MRI (BOLD fMRI) methods, so the unit of measurement for our purposes is the fMRI time series signal in each voxel the concern regarding data parcellation applies to a number of methods. The goal is to develop a representative brain network, with regions-of-interest (ROIs) determined at the voxel level, in the service of examining change in the relationships between regions over time, i.e., plasticity
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