Brains of endurance athletes differ in the association areas but not in the primary areas

Regular participation in sports results in a series of physiological adaptations. However, little is known about the brain adaptations to physical activity. Here we aimed to investigate whether young endurance athletes and non-athletes differ in the gray and white matter of the brain and whether cardiorespiratory fitness (CRF) is associated with these differences. We assessed the CRF, volumes of the gray and white matter of the brain using structural magnetic resonance imaging (sMRI), and brain white matter connections using diffusion magnetic resonance imaging (dMRI) in 20 young male endurance athletes and 21 healthy non-athletes. While total brain volume was similar in both groups, the white matter volume was larger and the gray matter volume was smaller in the athletes compared to non-athletes. The reduction of gray matter was located in the association areas of the brain that are specialized in processing of sensory stimuli. In the microstructure analysis, significant group differences were found only in the association tracts, for example, the inferior occipito-frontal fascicle (IOFF) showing higher fractional anisotropy and lower radial diffusivity, indicating stronger myelination in this tract. Additionally, gray and white matter brain volumes, as well as association tracts correlated with CRF. No changes were observed in other brain areas or tracts. In summary, the brain signature of the endurance athlete is characterized by changes in the integration of sensory and motor information in the association areas.

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.

U-fiber analysis: a toolbox for automated quantification of U-fibers and white matter hyperintensities

Background

Whether white matter hyperintensities (WMHs) involve U-fibers is of great value in understanding the different etiologies of cerebral white matter (WM) lesions. However, clinical practice currently relies only on the naked eye to determine whether WMHs are in the vicinity of U-fibers, and there is a lack of good neuroimaging tools to quantify WMHs and U-fibers.

Methods

Here, we developed a multimodal neuroimaging toolbox named U-fiber analysis (UFA) that can automatically extract WMHs and quantitatively characterize the volume and number of WMHs in different brain regions. In addition, we proposed an anatomically constrained U-fiber tracking scheme and quantitatively characterized the microstructure diffusion properties, fiber length, and number of U-fibers in different brain regions to help clinicians to quantitatively determine whether WMHs in the proximal cortex disrupt the microstructure of U-fibers. To validate the utility of the UFA toolbox, we analyzed the neuroimaging data from 246 patients with cerebral small vessel disease (cSVD) enrolled at Zhongshan Hospital between March 2018 and November 2019 in a cross-sectional study.

Results

According to the manual judgment of the clinician, the patients with cSVD were divided into a WMHs involved U-fiber group (U-fiber-involved group, 51 cases) and WMHs not involved U-fiber group (U-fiber-spared group, 163 cases). There were no significant differences between the U-fiber-spared group and the U-fiber-involved group in terms of age (P=0.143), gender (P=0.462), education (P=0.151), Mini-Mental State Examination (MMSE) scores (P=0.151), and Montreal Cognitive Assessment (MoCA) scores (P=0.411). However, patients in the U-fiber-involved group had higher Fazekas scores (P<0.001) and significantly higher whole brain WMHs (P=0.046) and deep WMH volumes (P<0.001) compared to patients in the U-fiber-spared group. Moreover, the U-fiber-involved group had higher WMH volumes in the bilateral frontal [P(left) <0.001, P(right) <0.001] and parietal lobes [P(left) <0.001, P(right) <0.001]. On the other hand, patients in the U-fiber-involved group had higher mean diffusivity (MD) and axial diffusivity (AD) in the bilateral parietal [P(left, MD) =0.048, P(right, MD) =0.045, P(left, AD) =0.015, P(right, AD) =0.015] and right frontal-parietal regions [P(MD) =0.048, P(AD) =0.027], and had significantly reduced mean fiber length and number in the right parietal [P(length) =0.013, P(number) =0.028] and right frontal-parietal regions [P(length) =0.048] compared to patients in the U-fiber-spared group.

Conclusions

Our results suggest that WMHs in the proximal cortex may disrupt the microstructure of U-fibers. Our tool may provide new insights into the understanding of WM lesions of different etiologies in the brain.

Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk

Research studies based on tractography have revealed a prominent reduction of asymmetry in some key white-matter tracts in schizophrenia (SCZ). However, we know little about the influence of common genetic risk factors for SCZ on the efficiency of routing on structural brain networks (SBNs). Here, we use a novel recall-by-genotype approach, where we sample young adults from a population-based cohort (ALSPAC:N genotyped = 8,365) based on their burden of common SCZ risk alleles as defined by polygenic risk score (PRS). We compared 181 individuals at extremes of low (N = 91) or high (N = 90) SCZ-PRS under a robust diffusion MRI-based graph theoretical SBN framework. We applied a semi-metric analysis revealing higher SMR values for the high SCZ-PRS group compared with the low SCZ-PRS group in the left hemisphere. Furthermore, a hemispheric asymmetry index showed a higher leftward preponderance of indirect connections for the high SCZ-PRS group compared with the low SCZ-PRS group (PFDR < 0.05). These findings might indicate less efficient structural connectivity in the higher genetic risk group. This is the first study in a population-based sample that reveals differences in the efficiency of SBNs associated with common genetic risk variants for SCZ.

Microstructural alterations in medial forebrain bundle are associated with interindividual pain sensitivity

The perception of pain to noxious stimuli, also known as pain sensitivity, varies among individuals. The comprised brain structures and their white matter pathways are complex and elusive. Here, we aimed to investigate whether variation of microstructure of the medial forebrain bundle (MFB), a tract connecting the basal forebrain with the brain stem, is associated with interindividual pain sensitivity. We assessed interindividual pain sensitivity as a rating of pain intensity to heat stimuli (45, 47, and 48.9°C) in 38 healthy men (age: 27.05 ± 5.7 years). We also reconstructed the MFB using multitensor tractography from diffusion magnetic resonance imaging (dMRI) and calculated free‐water corrected dMRI measures of fractional anisotropy (FA_t), radial diffusivity (RD_t), and axial diffusivity (AD_t). Lower ratings of interindividual pain intensity correlated with higher FA_t and lower RD_t of the MFB. As changes in FA_t and RD_t may reflect abnormalities in myelination, the results might be interpreted as that a lower pain rating is associated with higher degree of myelination of the MFB and could represent an inhibitory pathway of pain. Our results suggest that alteration of microstructure in the MFB contributes to the interindividual variation of pain perception.

White Matter Tract Segmentation as Multiple Linear Assignment Problems

Diffusion magnetic resonance imaging (dMRI) allows to reconstruct the main pathways of axons within the white matter of the brain as a set of polylines, called streamlines. The set of streamlines of the whole brain is called the tractogram. Organizing tractograms into anatomically meaningful structures, called tracts, is known as the tract segmentation problem, with important applications to neurosurgical planning and tractometry. Automatic tract segmentation techniques can be unsupervised or supervised. A common criticism of unsupervised methods, like clustering, is that there is no guarantee to obtain anatomically meaningful tracts. In this work, we focus on supervised tract segmentation, which is driven by prior knowledge from anatomical atlases or from examples, i.e., segmented tracts from different subjects. We present a supervised tract segmentation method that segments a given tract of interest in the tractogram of a new subject using multiple examples as prior information. Our proposed tract segmentation method is based on the idea of streamline correspondence i.e., on finding corresponding streamlines across different tractograms. In the literature, streamline correspondence has been addressed with the nearest neighbor (NN) strategy. Differently, here we formulate the problem of streamline correspondence as a linear assignment problem (LAP), which is a cornerstone of combinatorial optimization. With respect to the NN, the LAP introduces a constraint of one-to-one correspondence between streamlines, that forces the correspondences to follow the local anatomical differences between the example and the target tract, neglected by the NN. In the proposed solution, we combined the Jonker-Volgenant algorithm (LAPJV) for solving the LAP together with an efficient way of computing the nearest neighbors of a streamline, which massively reduces the total amount of computations needed to segment a tract. Moreover, we propose a ranking strategy to merge correspondences coming from different examples. We validate the proposed method on tractograms generated from the human connectome project (HCP) dataset and compare the segmentations with the NN method and the ROI-based method. The results show that LAP-based segmentation is vastly more accurate than ROI-based segmentation and substantially more accurate than the NN strategy. We provide a Free/OpenSource implementation of the proposed method.