Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Menstrual Cycle-Related Changes in Women with Schizophrenia: A Resting-State fMRI Study.

INTRODUCTION: Different influences of ovarian hormones in schizophrenia (SCZ) have been reported, but no study to date has assessed their effects on the brain dynamics at rest. The present study aimed to examine the hormonal and clinical changes related to the menstrual cycle and alterations in the resting-state functional connectivity (RS-FC) depending on cycle phase and/or hormonal fluctuations in SCZ. METHOD: This study was conducted based on both between- and within-subject experimental designs, including 13 clinically stable female patients with SCZ (32 ± 7.7 years) and 13 healthy women (30 ± 7.3 years). RS-functional magnetic resonance imaging (fMRI) scanning, as well as hormonal and clinical assessments, was applied to each participant twice during two cycle phases: early follicular and mid-luteal. RESULTS: A difference in mid-luteal progesterone levels was found between groups, with a large effect size (Cohen's d) of 0.8 (p < 0.05). Also, the estradiol levels negatively correlated with the negative symptom severity of the patients during their mid-luteal phase. In the patients, estrogen positively correlated with the auditory network connectivity in the left amygdala during the early follicular phase. In the controls, progesterone had positive correlations with the connectivity of the posterior default mode and the left frontoparietal networks in the bilateral precuneus during the early follicular phase and had a negative correlation with the executive control network connectivity in the mid-luteal phase. CONCLUSION: The present study showed hormonal differences between groups and suggested that the levels of cycle-dependent hormones might be associated with the changes in clinical symptom severity and the RS-FC in the groups. Our RS-fMRI findings warrant further investigation.

Cellular Automata Tractography: Fast Geodesic Diffusion MR Tractography and Connectivity Based Segmentation on the GPU.

Geodesic based tractography on diffusion magnetic resonance data is a method to devise long distance connectivities among the brain regions. In this study, cellular automata technique is applied to the geodesic tractography problem and the algorithm is implemented on a graphics processing unit. Cellular automaton based method is preferable to current techniques due to its parallel nature and ability to solve the connectivity based segmentation problem with the same computational complexity, which has important applications in neuroimaging. An application to prior-less tracking and connectivity based segmentation of corpus callosum fibers is presented as an example. A geodesic tractography based corpus callosum atlas is provided, which reveals high projections to the cortical language areas. The developed method not only allows fast computation especially for segmentation but also provides a powerful and intuitive framework, suitable to derive new algorithms to perform connectivity calculations and allowing novel applications.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

Working memory dysfunction in delusional disorders: an fMRI investigation.

BACKGROUND: Delusional disorder (DD) is a rare and understudied psychiatric disorder. There is limited number of studies concerning cognitive characteristics in DD. Using an established working memory paradigm with variable levels of memory load, we investigated alterations in functional magnetic resonance imaging (fMRI) of brain regions in patients with DD. METHODS: This case control study included 9 patients with DD and 9 healthy control subjects matched for age, sex, and education level. Diagnosis of DD was confirmed using the Structured Clinical Interview for DSM-IV Axis I. The severity of the symptoms was evaluated using the Positive and Negative Syndrome Scale. All patients were asked to perform 0-back and 2-back tasks during fMRI experiments. Functional imaging was performed using the 3.0 T Philips whole-body scanner using an 8-channel head coil. RESULTS: Participants with DD had less neural activation of the left dorsolateral prefrontal cortex in fMRI scans obtained during performance tasks. On the other hand, neural activation of the left and right superior temporal gyrus, left middle and inferior temporal gyrus, right and left posterior cingulate gyrus, right amygdala, left and right fusiform gyrus was more prominent in patients with DD in comparison with the control group. DISCUSSION: Patients with DD had dysfunction in the prefrontal, temporal and limbic regions of the brain in particular, during performance tasks of working memory. Our findings were in line with the findings of the early reports on deficient functioning in temporal or limbic regions of the brain. Further, patients with DD displayed prefrontal dysfunction as seen in patients with schizophrenia.

Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications.

In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.

Cellular automata segmentation of brain tumors on post contrast MR images.

In this paper, we re-examine the cellular automata (CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.