Individual Functional ROI Optimization Via Maximization of Group-Wise Consistency of Structural and Functional Profiles

Abstract

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain.

Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles

“…In this paper, we adopted the first-round optimization using the method in [24] as our input, meaning that we had the initial locations of the ROIs. Then, we applied our algorithmic pipeline outlined in Fig.…”

“…Hence, the maximization of structural connectivity consistency reflects the maximization of functional correspondence to a certain extent. We used fifteen subjects randomly selected from a recent study [24]. fMRI data was acquired for each subject for 2 runs.…”

“…Acquisition parameters were as follows: fMRI: 64×64 matrix, 4mm slice thickness, 220mm FOV, 30 slices, TR=1.5s, TE=25ms, ASSET=2; DTI: 128×128 matrix, 2mm slice thickness, 256mm FOV, 60 slices, TR=15100ms, TE=variable, ASSET=2, 3 B0 images, 30 optimized gradient directions, b-value=1000; all aligned to the AC-PC line. Preprocessing steps are referred to [24].…”

Predicting Functional Brain ROIs via Fiber Shape Models

“…Then, descriptive shape models and ROIs loc (the green box in Fig. 1), the data by minimizing an energ based fMRI data to validate wever, in our view, this task is challenging due to the unc al regions, remarkable variability of brain anatomy, nstance, a slight change of the shape, size or location o er its structural and functional connectivity profiles [6]. nt a novel framework that learns fiber shape model del of functional ROIs based on multimodal task-ba training stage, and apply the predictive models to loca samples based only on DTI data.…”

“…In this paper, we presen anatomical constraint mod fMRI and DTI data in the functional ROIs in testing s this framework and our c training stage, the activated the benchmark ROI data emanating from these RO fMRI data [6] demonstrat functional ROIs are quite c connection pattern is a goo the "connectional fingerpri only DTI data is needed t predictive models, withou Typically, a DTI scan need widely available. Therefore applications in brain imagin…”