Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination

Sex-related variability of white matter tracts in the whole HCP cohort

Behavioral differences between men and women have been studied extensively, as have differences in brain anatomy. However, most studies have focused on differences in gray matter, while white matter has been much less studied. We conducted a comprehensive study of 77 deep white matter tracts to analyze their volumetric and microstructural variability between men and women in the full Human Connectome Project (HCP) cohort of 1065 healthy individuals aged 22-35 years. We found a significant difference in total brain volume between men and women (+ 12.6% in men), consistent with the literature. 16 tracts showed significant volumetric differences between men and women, one of which stood out due to a larger effect size: the corpus callosum genu, which was larger in women (+ 7.3% in women, p = 5.76 × 10-19). In addition, we found several differences in microstructural parameters between men and women, both using standard Diffusion Tensor Imaging (DTI) parameters and more complex microstructural parameters from the Neurite Orientation Dispersion and Density Imaging (NODDI) model, with the tracts showing the greatest differences belonging to motor (cortico-spinal tracts, cortico-cerebellar tracts) or limbic (cingulum, fornix, thalamo-temporal radiations) systems. These microstructural differences may be related to known behavioral differences between the sexes in timed motor performance, aggressiveness/impulsivity, and social cognition.

A Practical Roadmap to Implementing Deep Learning Segmentation in the Clinical Neuroimaging Research Workflow

BACKGROUND

Thanks to the proliferation of open-source tools, we are seeing an exponential growth of machine-learning applications, and its integration has become more accessible, particularly for segmentation tools in neuroimaging.

METHODS

This article explores a generalized methodology that harnesses these tools and aims/enables to expedite and enhance the reproducibility of clinical research. Herein, critical considerations include hardware, software, neural network training strategies, and data labeling guidelines. More specifically, we advocate an iterative approach to model training and transfer learning, focusing on internal validation and outlier handling early in the labeling process and fine-tuning later.

RESULTS

The iterative refinement process allows experts to intervene and improve model reliability while cutting down on their time spent in manual work. A seamless integration of the final model's predictions into clinical research is proposed to ensure standardized and reproducible results.

CONCLUSIONS

In short, this article provides a comprehensive framework for accelerating research using machine-learning techniques for image segmentation.

Multimodal study of multilevel pulvino-temporal connections: a new piece in the puzzle of lexical retrieval networks

Advanced methods of imaging and mapping the healthy and lesioned brain have allowed for the identification of the cortical nodes and white matter tracts supporting the dual neurofunctional organization of language networks in a dorsal phonological and a ventral semantic stream. Much less understood are the anatomical correlates of the interaction between the two streams; one hypothesis being that of a subcortically mediated interaction, through crossed cortico-striato-thalamo-cortical and cortico-thalamo-cortical loops. In this regard, the pulvinar is the thalamic subdivision that has most regularly appeared as implicated in the processing of lexical retrieval. However, descriptions of its connections with temporal (language) areas remain scarce. Here we assess this pulvino-temporal connectivity using a combination of state-of-the-art techniques: white matter stimulation in awake surgery and postoperative diffusion MRI (n = 4), virtual dissection from the Human Connectome Project 3 and 7 T datasets (n = 172) and operative microscope-assisted post-mortem fibre dissection (n = 12). We demonstrate the presence of four fundamental fibre contingents: (i) the anterior component (Arnold's bundle proper) initially described by Arnold in the 19th century and destined to the anterior temporal lobe; (ii) the optic radiations-like component, which leaves the pulvinar accompanying the optical radiations and reaches the posterior basal temporal cortices; (iii) the lateral component, which crosses the temporal stem orthogonally and reaches the middle temporal gyrus; and (iv) the auditory radiations-like component, which leaves the pulvinar accompanying the auditory radiations to the superomedial aspect of the temporal operculum, just posteriorly to Heschl's gyrus. Each of those components might correspond to a different level of information processing involved in the lexical retrieval process of picture naming.

Blurred streamlines: A novel representation to reduce redundancy in tractography

Tractography is a powerful tool to study brain connectivity in vivo, but it is well known to suffer from an intrinsic trade-off between sensitivity and specificity. A critical - but usually underrated - parameter to choose that can heavily impact the quality of the estimates is the number of streamlines to be reconstructed for a given data set. In fact, sensitivity can be improved by generating more and more streamlines, as all real anatomical connections are likely reconstructed, but lots of false positives are inevitably introduced, too. Consequently, so-called tractography filtering techniques have become increasingly popular to get rid of these false positives and improve specificity. However, increasing number of streamlines introduces redundancy in tractography reconstructions, which may negatively impact the performance of filtering algorithms, especially those based on linear formulations. To address this problem, we introduce a novel streamlines representation, called "blurred streamlines", which drastically reduces the redundancy among streamlines by (i) clustering similar trajectories and (ii) spatially blurring the corresponding signal contributions. We tested the effectiveness of the blurred streamlines both on synthetic and in vivo data. Our results clearly show that this new representation is as accurate as state-of-the-art methods despite using only 5% of the input streamlines, thus significantly decreasing the computational complexity of filtering algorithms as well as storage requirements of the resulting reconstructions.

A systematic review of automated methods to perform white matter tract segmentation

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.

FIESTA: Autoencoders for accurate fiber segmentation in tractography

White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework.

The influence of regions of interest on tractography virtual dissection protocols: general principles to learn and to follow

Efficient communication across fields of research is challenging, especially when they are at opposite ends of the physical and digital spectrum. Neuroanatomy and neuroimaging may seem close to each other. When neuroimaging studies try to isolate structures of interest, according to a specific anatomical definition, a variety of challenges emerge. It is a non-trivial task to convert the neuroanatomical knowledge to instructions and rules to be executed in neuroimaging software. In the process called "virtual dissection" used to isolate coherent white matter structure in tractography, each white matter pathway has its own set of landmarks (regions of interest) used as inclusion and exclusion criteria. The ability to segment and study these pathways is critical for scientific progress, yet, variability may depend on region placement, and be influenced by the person positioning the region (i.e., a rater). When raters' variability is taken into account, the impact made by each region of interest becomes even more difficult to interpret. A delicate balance between anatomical validity, impact on the virtual dissection and raters' reproducibility emerge. In this work, we investigate this balance by leveraging manual delineation data of a group of raters from a previous study to quantify which set of landmarks and criteria contribute most to variability in virtual dissection. To supplement our analysis, the variability of each pathway with a region-by-region exploration was performed. We present a detailed exploration and description of each region, the causes of variability and its impacts. Finally, we provide a brief overview of the lessons learned from our previous virtual dissection projects and propose recommendations for future virtual dissection protocols as well as perspectives to reach better community agreement when it comes to anatomical definitions of white matter pathways.