Functional brain segmentation using inter-subject correlation in fMRI.

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily-life situations. A new exploratory data-analysis approach, functional segmentation inter-subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable. FuSeISC was tested using fMRI data sets collected during traditional block-design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only useful for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but also has other potential applications, such as generation of a functional brain atlases including both lower- and higher-order processing areas. Finally, as a part of FuSeISC, a criterion-based sparsification of the shared nearest-neighbor graph was proposed for detecting clusters in noisy data. In the tests with synthetic data, this technique was superior to well-known clustering methods, such as Ward's method, affinity propagation, and K-means ++. Hum Brain Mapp 38:2643-2665, 2017. © 2017 Wiley Periodicals, Inc.

Retrospective in silico evaluation of optimized preoperative planning for temporal bone surgery.

PURPOSE: Robot-assisted surgery at the temporal bone utilizing a flexible drilling unit would allow safer access to clinical targets such as the cochlea or the internal auditory canal by navigating along nonlinear trajectories. One key sub-step for clinical realization of such a procedure is automated preoperative surgical planning that incorporates both segmentation of risk structures and optimized trajectory planning. METHODS: We automatically segment risk structures using 3D U-Nets with probabilistic active shape models. For nonlinear trajectory planning, we adapt bidirectional rapidly exploring random trees on Bézier Splines followed by sequential convex optimization. Functional evaluation, assessing segmentation quality based on the subsequent trajectory planning step, shows the suitability of our novel segmentation approach for this two-step preoperative pipeline. RESULTS: Based on 24 data sets of the temporal bone, we perform a functional evaluation of preoperative surgical planning. Our experiments show that the automated segmentation provides safe and coherent surface models that can be used in collision detection during motion planning. The source code of the algorithms will be made publicly available. CONCLUSION: Optimized trajectory planning based on shape regularized segmentation leads to safe access canals for temporal bone surgery. Functional evaluation shows the promising results for both 3D U-Net and Bézier Spline trajectories.

Integration and segregation of functional segmented anterior and posterior hippocampal networks in memory performance.

PURPOSE: To determine the association between functional connectivity (FC) of functional-segmented anterior and posterior portions of the hippocampus and performance on verbal and visual memory tests in a young, healthy population. METHODS: We recruited 100 healthy participants in the age of 19-29. Resting state fMRI data were acquired and voxel-wise correlation analysis was performed to functionally divide the hippocampus. We investigated the inter-hemispheric hippocampal-cortical functional connectivity after the participants took the assessment of episodic memory using verbal (California Verbal Learning Test II, CVLT-II) and visual subtests (Rey-Osterrieth Complex Figure, ROCF). The partial correlations were used to identify the association between the intra-hemispheric hippocampal-cortical mean resting correlation and memory performance. RESULTS: The results showed that the anterior and posterior hippocampal networks involved differently in verbal and visual memory. Intra-hemispheric FC between left posterior hippocampus and posterior parahippocampal gyrus (PPHG) was positively correlated with CVLT-II Trail 2 Immediate Free Recall (r = 0.223, p = 0.029). Intra-hemispheric FC between left posterior hippocampus and posterior cingulate (PCC) was negatively correlated with ROCF Immediate Recall (r = -0.217 p = 0.034). Intra-hemispheric FC between left anterior hippocampus and temporal pole (TP) negatively correlated with ROCF Delayed Recall (r = -0.228, p = 0.025). Split half resampling procedure results showed some repeatability in our subjects. CONCLUSION: The present results demonstrated that, the anterior hippocampus was specifically involved in the visual memory processing, whereas the posterior hippocampus contributed to both the verbal and visual memories, which may have implications for a functionally synergetic and dissociable role of the hippocampus in different kinds of memory.

Functional segmentation of dynamic PET studies: Open source implementation and validation of a leader-follower-based algorithm.

UNLABELLED: We present a novel segmentation algorithm for dynamic PET studies that groups pixels according to the similarity of their time-activity curves. METHODS: Sixteen mice bearing a human tumor cell line xenograft (CH-157MN) were imaged with three different (68)Ga-DOTA-peptides (DOTANOC, DOTATATE, DOTATOC) using a small animal PET-CT scanner. Regional activities (input function and tumor) were obtained after manual delineation of regions of interest over the image. The algorithm was implemented under the jClustering framework and used to extract the same regional activities as in the manual approach. The volume of distribution in the tumor was computed using the Logan linear method. A Kruskal-Wallis test was used to investigate significant differences between the manually and automatically obtained volumes of distribution. RESULTS: The algorithm successfully segmented all the studies. No significant differences were found for the same tracer across different segmentation methods. Manual delineation revealed significant differences between DOTANOC and the other two tracers (DOTANOC - DOTATATE, p=0.020; DOTANOC - DOTATOC, p=0.033). Similar differences were found using the leader-follower algorithm. CONCLUSION: An open implementation of a novel segmentation method for dynamic PET studies is presented and validated in rodent studies. It successfully replicated the manual results obtained in small-animal studies, thus making it a reliable substitute for this task and, potentially, for other dynamic segmentation procedures.